Magnitude of diabetic retinopathy and its associated factors among patients with type 2 diabetes on follow up at diabetes clinic of Asella Referral and Teaching Hospital, Asella, Ethiopia: a cross-sectional study

Magnitude of diabetic retinopathy and its associated factors among patients with type 2 diabetes on follow up at diabetes clinic of Asella Referral and Teaching Hospital, Asella, Ethiopia: a cross-sectional 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 Magnitude of diabetic retinopathy and its associated factors among patients with type 2 diabetes on follow up at diabetes clinic of Asella Referral and Teaching Hospital, Asella, Ethiopia: a cross-sectional study Kidist Tadesse Bedada, Koricho Simie Tolla, Ashenafi Habtamu Regesu, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4231400/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 is a microvascular complication of diabetes mellitus that is among the leading causes of irreversible blindness worldwide. There is a growing concern about diabetic retinopathy in Ethiopia associated with the increasing prevalence of diabetes. Limited studies have been conducted in Ethiopia that evaluated the magnitude of diabetic retinopathy and its associated factors among patients with type 2 diabetes. Thus, we aimed to assess the magnitude of diabetic retinopathy and its associated factors among patients with type 2 diabetes on follow-up at the diabetic clinic of Asella Referral and Teaching Hospital. Methodology: An institution-based cross-sectional study involving 428 patients with type 2 diabetes was conducted at the diabetic clinic of Asella Referral and Teaching Hospital. Participants were selected using a systematic random sampling technique. A descriptive analysis was performed to determine the prevalence of diabetic retinopathy. Bivariate binary logistic regression and multivariable logistic regression were conducted to determine factors associated with diabetic retinopathy, at α = 0.05 using adjusted odds ratio with 95% confidence interval. Result Of the 428 participants, 53 (12.4%) had diabetic retinopathy (95% CI: 9.4, 15.9). Dyslipidemia (AOR = 2.538, 95% CI: 1.190, 5.412), poor medication adherence (AOR = 3.706, 95% CI: 1.730, 7.940), presence of other complications of diabetes (AOR = 2.537, 95% CI: 1.10, 5.850) were factors associated with diabetic retinopathy. Furthermore, those who exercise regularly (AOR = 0.048, 95% CI: 0.004, 0.565) had lower odds of having retinopathy than their counterpart. Conclusion The prevalence of diabetic retinopathy in this study is lower than previous studies. Dyslipidemia, regular exercise, medication adherence, and the presence of other diabetic complications were factors significantly associated with diabetic retinopathy. Health education is recommended to promote exercise self-care and diabetes medication adherence. Early screening for other comorbidities and diabetes complications is also recommended. Figures Figure 1 Introduction Diabetes mellitus (DM) refers to a group of common metabolic disorders that share the phenotype of hyperglycemia and affected more than half a billion people globally in 2021 [ 1 , 2 ]. In Ethiopia, the prevalence of Diabetes was estimated to be more than 5 million, which is among the highest in Africa, and with a concomitant high burden of undiagnosed DM [ 1 – 4 ]. Diabetic retinopathy is a microvascular complication of diabetes and is a major cause of impaired vision in patients aged between 20 and 74 years [ 5 ]. The International Diabetes Federation (IDF) estimated that more than 100 million people worldwide had DR in 2020, and is expected to show more than 50% rise by the year 2045 [ 3 ]. Of the global burden of DR, African countries such as Ethiopia take the highest share [ 3 , 6 , 7 ]. This estimate is significant considering the high prevalence of undiagnosed diabetic population in the continent [ 3 , 7 ]. Studies conducted in Ethiopia among diabetic patients, both type 1 and type 2, reported magnitudes of DR ranging from 4.7–36.3% [ 4 , 8 ]. However, this figure could be under representative given the high burden of undiagnosed diabetes, the poor health care seeking behavior, and the low diabetic retinopathy awareness [ 9 ]. In Ethiopia, the majority of diabetic patients are Type 2 DM [ 4 ]. literature show that more than two-thirds of patients with type 2 diabetes develop diabetic retinopathy in the first two decades of being diagnosed with DM [ 5 ]. However, to the best of the authors’ knowledge, only one study examined risk factors for DR among patients with Type 2 DM in Ethiopia [ 8 ]. Furthermore, previous studies did not include important self-care and behavioral variables such as medication adherence, alcohol consumption, and exercise, as well as important comorbidity profiles such as dyslipidemia and proteinuria. Thus, this study would add to the limited body of evidence available on DR among type 2 diabetes patients in Ethiopia and could serve as a baseline study up on which other researchers interested in the area can broaden. Therefore, this study aimed to assess the magnitude of diabetic retinopathy and its associated factor among patients with type 2 diabetes on follow-up at diabetic clinic of Asella Referral and Teaching Hospital. Methods and materials Study setting and study design The study was conducted at Arsi University, College of Health Science, Asella Teaching and Referral Hospital, located 175 kilometers from Addis Ababa, the capital of Ethiopia. The hospital is the largest hospital in the area serving more than 3.5 million populations. The chronic follow-up clinic at ARTH serves a total of 2800 Type 2 diabetic patients and is staffed with 1 internist, 2 internal medicine residents covering the clinic with rotation, and 4 nurses. There are also 3 ophthalmologists, 3 optometrists, and 4 ophthalmology nurses in the hospital. An institution-based cross-sectional study design was conducted from 11-December-2023 to 12-January-2024. Source and study population Adult type 2 diabetes patients on follow-up at ARTH DM clinic were the source population, while all adult type 2 diabetes patients on follow-up at ARTH DM clinic who visited during the study period were the study population. Inclusion and exclusion criteria All adults with type 2 diabetes mellitus aged ≥ 18 years old who are on follow-up were eligible for this study. Patients who were critically ill and unable to communicate during data collection and those patients with other types of DM such as Type 1 and gestational diabetes were excluded from the study. Sample size and sampling procedure The sample size was determined using Epi info version 7 using the following assumptions: 80% power, proportion of DR among those without hypertension = 0.065 [ 8 ], AOR = 2.56 [ 8 ], 95% confidence interval, and total population(N) = 2800. A final sample size of 428 was achieved after accounting for finite population correction and 10% non-response. A systematic random sampling technique using a sampling interval of 7 was used to select study subjects. When patients do not meet the inclusion criteria, the next patient who meets the criteria was included. Patients’ medical record number were listed accordingly and marked to avoid patient selection more than once. Data collection technique and quality control Data were collected through chart review using a checklist, an interviewer administered questionnaire. The tool was translated into the local Afaan oromo language and back translated to English to check for consistency. The data collection tool was pretested on 5% of the sample size at Adama Hospital medical college. Based on the pretest findings, corrections were made to the data extraction form. Data collection was done by two trained nurses and the supervision of the overall data collection activity of the study was carried out by the investigators. Prior to the data collection, a one day orientation was given to the data collectors. The completed data were randomly selected and cross-checked with medical records of the patients to ensure consistency at the end of each data collection day. Operational definition Diabetic retinopathy: Retinopathy was determined by the evaluating ophthalmologist after slit lamp biomicroscopy evaluation. Evaluation by an ophthalmologist was performed using a slit lamp microscopy and a 90D condensing lens after dilating the pupil with 1% Tropicamide. Medication adherence: adherence was assessed using the Morisky Medication Adherence Scale (MMAS-4). For the four questions, ‘yes’ was scored as 0 and ‘no’ was scored as 1. The total score was found after summing these scores and labeled as poor if the total score is 1, moderate = 2–3, and good if scored 4. [ 10 ] Moderate and poor were categorized as poor for the logistic regression analysis. Glycemic control: Fasting blood sugar (FBS) measurement 70–126 mg/dl on the last follow up was used to label as having good glycemic control. Those with FBS 126 mg/dl were labeled as having poor glycemic control [ 11 ]. Hypertension control: a systolic blood pressure < 140 mm hg and diastolic blood pressure 200 mg/dl and/or high density lipoprotein level < 40 mg/dl for male < 50 mg/dl for females and and/or low density lipoprotein level ≥ 100 mg/dl and/or triglycerides ≥ 150 mg/dl. [ 5 ] Obesity: Body Mass Index (BMI) was classified as underweight ( = 30kg/m 2 ) [ 13 ]. Alcohol consumption: reported consumption of alcohol consumption with in the 6 months prior to data collection. Regular exercise: 30 minute vigorous intensity physical activity for 3 days or more per week apart from routine work related activity was labeled as regular exercise [ 11 ]. Data processing and analysis Data entry, coding and verification were done using Epi-data 3.1 and data analysis was performed using SPSS version 26. Descriptive analysis was performed to describe the socio-demographic characteristics and clinical profile of the participants. Categorical variables were summarized using frequency and percentage, while continuous variables were summarized using mean and standard deviation. Binary logistic regression and multivariable logistic regression were performed to determine associated factors of diabetic retinopathy among Type 2 diabetic patients, using α = 0.05 as significance level. Those variables with a p-value < 0.25 were considered for multivariable logistic regression. The association was measured using adjusted odds ratio (AOR) with the corresponding confidence interval. Association with p-value less than 0.05 were considered significant. Model goodness of fit was assessed using Hosmer and Lemshow goodness of fit test. Ethical statement Ethical approval letter was obtained from Arsi University, Asella referral and teaching hospital Institution Review Board (A/CHS/RC/63/2023). A formal letter from the department of medicine was sent to the participating departments. Informed written consent was obtained from the participants. The retrieved data were kept strictly confidential and the names of the patients were not included in the study. Results Socio-demographic characteristics In this study, more than a third (38.1%) of the participants were in the age group of 45-55 years. Moreover, more than half of the respondents were male (56.3%) and more than two-third (73.6%) of the participants were married. Regarding educational status, 104 (24.3%) had secondary education and 70 (16.4%) had college or higher education. Based on occupation, 105 (24.5%) were farmers and 96 (22.4%) were house wives. Furthermore, the majority of the participants (66.1%) were from an urban area. Additionally, more than a third of the participants (34.6%) had income ≥7500 Ethiopian birr. ( Table 1 ) Table 1. Socio-demographic profile of patients with Type 2 diabetes at ASRTH, 2023 Variables Frequency (N=428) Percentage Age group 65 67 15.7 Sex Male 241 56.3 Female 187 43.7 Marital status Single 46 10.7 Married 315 73.6 Divorced 54 12.6 Widowed 13 3 Education status Unable to read and write 53 12.4 Read and write 120 28 primary school 81 18.9 Secondary school 104 24.3 College and above 70 16.4 Occupation Government employee 56 13.1 Non-governmental employee 73 17.1 Merchant 36 8.4 Farmer 105 24.5 Retired 56 13.1 Housewife 96 22.4 Others 6 1.4 Income 1500-3499 77 18 3500-5499 151 35.3 5500-7499 52 12.1 ≥7500 148 34.6 Residence Urban 283 66.1 Rural 145 33.9 Others; daily laborers, priests Comorbidity profile of participants Among the participants, 160 (37.4%) have hypertension, and of those who have hypertension 86 (53.8%) have controlled hypertension. Around 175 (41%) patients have dyslipidemia and 293 (68.5%) are on statin treatment. Regarding BMI, more than one-third (164; 38.3%) were overweight and 43 (10%) participants were obese. Moreover, 60 (14%) participants had + 2 or more proteinuria and the mean creatinine was 0.849 (SD: ±0.362). ( Table 2 ) Self-care and behavioral profile of patients It was found that 37 (8.6%) of the participants had history of ever smoking, while only 4 of the participants were current smokers. Meanwhile, 47 (11%) of the respondents consumed alcohol in the past 6 months and the majority of them consumed beer (22; 46.8%) and Tella (19; 40.4%). Of the respondents, 161(37.6%) have a glucometer at home and 362 (84.6%) have received diabetes education. Regarding medication adherence, 326 (76.2%) have good medication adherence while only 5 (1.2%) have poor adherence. ( Table 3 ) Table 3. Self-care and behavioral profile of patients with type 2 diabetes on follow-up at ASRTH, 2023 Factors Frequency Percentage Ever smoked Yes 37 8.6 No 391 91.4 Regular exercise Yes 57 13.3 No 371 86.7 Past 6 month Alcohol consumption Yes 47 11 No 256 89 Type of alcohol consumed (n=47) Local areke 6 12.8 Tella 19 40.4 Beer 22 46.8 Have a glucometer at home Yes 161 37.6 No 267 62.4 Received diabetic health education 173 70 Yes 362 84.6 No 66 15.4 Medication adherence Good 326 76.2 Moderate 97 22.7 Poor 5 1.2 Diabetic profile of patients More than two-third of the participants in this study were diagnosed with type 2 diabetes less than 10 year back. Of the respondents, 296 (69.2%) had poor glycemic control. In addition, nearly half of them were on oral glucose lowering agents (213; 49.8%) and had one or more diabetes complication (211; 49.3%). Diabetic neuropathy (83; 39.3%) was the most common complication followed by cardiovascular complications (61; 28.9%) ( Table 4 ). Table 4. Diabetes related profile of type 2 diabetes patients on follow up at ASTRH, 202 3 Variables Frequency Percentage Duration of diabetes (years) 1-4 154 36 5-9 157 36.7 ≥10 117 27.3 Glycemic control Good 132 30.8 poor 296 69.2 Type of medication Oral 213 49.8 Insulin 120 28.0 Mixed 95 22.2 Diabetes related complications Yes 211 49.3 No 217 50.7 Type of DM complication other than DR Cardiovascular 61 28.9 Chronic kidney disease 31 14.7 Neuropathy 83 39.3 Cerebrovascular 11 5.2 PAD 8 3.8 Diabetic foot complication 6 2.8 Others 11 5.2 Others; erectile dysfunction, cataract PAD; peripheral arterial disease Magnitude of Diabetic retinopathy Among the participants, 53(12.4%) had diabetic retinopathy (95% CI: 9.4, 15.9). ( Fig 1) Fig 1: Magnitude of diabetic retinopathy among type 2 diabetes patient in ASTRH, 2023 Factors associated with DR among patients with type 2 diabetes After computing bivariate binary logistic regression, sex, age, marital status, smoking, alcohol consumption, exercise, medication adherence, glycemic control, mode of treatment, BMI, proteinuria, presence of diabetic complications, hypertension, dyslipidemia, and statin use were found to be candidate variables (p- value <0.25) for multivariable binary logistic regression. Subsequently, Multivariable logistic regression was performed and it was found that, after adjusting for other variables, dyslipidemia, exercise, medication adherence, and the presence of other DM complications were statistically significant predictors of diabetic retinopathy among patients with type 2 diabetes. Participants with dyslipidemia had 2.538 times higher odds of having DR than those without dyslipidemia (AOR=2.538, 95% CI: 1.190, 5.412). Meanwhile, those who exercise regularly had 95.2% lower odds of having DR compared to those who do not (AOR=0.048, 95%: 0.004, 0.565). Moreover, those patients who had poor adherence to medication had 3.7 times higher odds of having DR than those who have good adherence (AOR=3.706, 95% CI: 1.730, 7.940). Furthermore, those who have diabetes complication other than DR have 2.54 times higher odds of having DR compared to those who do not (AOR=2.537, 95% CI: 1.10, 5.850). ( Table 5 ) Table 5. Bivariate and multivariable analysis of factors associated with diabetic retinopathy among patients with type 2 diabetes on follow-up at ASRTH, 2023 Variables Diabetic retinopathy COR (95%C.I) AOR (95%CI) p-value Yes No Age group 0.087 65 7(10.4%) 60(89.6%) 0.636( 0.231, 1.752) 0.278(0.069, 1.121) 0.072 Sex Male 37(15.4%) 204(84.6%) 1.938(1.042,1.606) 1.442(0.675,3.083) 0.345 Female 16(8.6%) 171(91.4%) 1.00 1 Marital status 0.222 Single 10(21.7%) 36(78.3%) 1.389(0.510,3.781) 1.526(0.388,5.998) 0.545 Married 33(10.5%) 282(89.5%) 0.585(0.263,1.304) 0.56(0.196,1.597) 0.278 Widowed 1(7.7%) 12(92.3%) 0.417(0.048,3.628) 0.384(0.033,4.478) 0.445 Divorced 9(16.7%) 45(83.3%) 1 1 Hypertension Yes 30(18.8%) 130(81.2%) 2.458(1.372,4.405) 2.036(0.95,4.365) 0.068 No 23(8.6%) 36(91.4%) 1 1 Dyslipidemia Yes 34(19.4%) 141(80.6%) 2.97(1.631,5.406) 2.538(1.190, 5.412) 0.016 * No 19(7.5%) 234(92.5%) 1 1 Proteinuria 0.789 Normal 24(9.6%) 225(90.4%) 1 1 Trace 4(6.2%) 60(93.8%) 0.625(0.209,1.870) 0.622(0.260,1.484) 0.284 +1 9(16.4%) 46(83.6%) 1.834(0.801,4.203) 0.213(0.053,0.856) 0.029 ≥+2 16(26.7%) 44(73.3%) 3.409(1.676,6.936) 1.166(0.376, 3.622) 0.790 BMI 0.055 Underweight and normal 25(11.3%) 196(88.7%) 1 1 Overweight 18(11%) 146(89%) 0.967 (0.172,0.962) 0.62(0.277,1.358) 0.243 Obese 10(23.3%) 33(76.7%) 2.376(1.045, 5.399) 2.323(0.839,6.434) 0.105 Statin use Yes 31(10.6%) 262(89.4%) 0.608(0.337,1.096) 1.219(0.544,2.732) 0.630 No 22(16.3%) 113(83.7%) 1.00 1.00 Smoking Yes 9(24.3%) 28(75.7%) 2.535(1.123,5.720) 1.836(0.622,5.420) 0.271 No 44(11.3%) 75(88.7%) 1.00 1.00 Alcohol consumption Yes 12(22.5%) 35(77.5%) 2.843(1.368,5.908) 1.850(0.648,5.282) 0.251 No 41(10.8%) 340(89.2%) 1.00 Exercise Yes 1(1.8%) 56(98.2%) 0.11 (0.015,0.809) 0.048(0.004,0.565) 0.016 * No 52(14%) 319(86%) 1.00 Adherence Good 40(50%) 40(50%) 1 1 Poor 137(62.8%) 81(37.2%) 3.455(1.907,6.263) 3.706(1.730,7.940) 0.001 * Glycemic control Good 28(8.6%) 298(91.4%) 0.482(0.234,0.992) 0.899(0.365,2.212) 0.816 Poor 25(24.5%) 77(75.5%) 1.00 1 Mode of treatment 0.091 Insulin 22(18.3%) 98(81.7%) 1.553(0.725, 3.326) 1.5(0.564,3.989) 0.417 Oral and insulin 12(12.6%) 83(87.4%) 1.00 1 Oral 19(8.9%) 194(91.1%) 0.677(0.315, 1.459) 0.568(0.228,1.414) 0.224 Diabetic complication Yes 36(17.1%) 175(82.9%) 2.420(1.313,4.460) 2.537(1.10,5.850) 0.029 * No 17(7.8%) 200(92.2%) 1 1 Discussion The study assessed the magnitude of diabetic retinopathy and its associated factors among 428 patients with type 2 diabetes on follow-up at ASRTH diabetic clinic. The magnitude of diabetic retinopathy was found to be 12.4%. This finding is comparable with a study conducted in Spain that reported 12.3% DR [14]. However, it was higher than studies done in Denmark (6.8%) [15] and lower than studies done in Northwest Ethiopia (36.3%) [8], Northern Ethiopia (16%) [12], Indonesia (43.1%) [16], Malaysia (39.3%) [17], Iran (37.8%) [18], Saudi Arabia (19.7%) [19], and United Kingdom (18%) [20]. This difference in the prevalence of diabetic retinopathy could be attributed to differences in sample size, baseline comorbidity status, and other diabetic complications. Patients with type 2 diabetes who had dyslipidemia were also found to have higher odds of diabetic retinopathy compared to their counterparts. This finding is supported by studies done in Germany [21], Taiwan [22], Malaysia [17], Italy [23], China [24], and Thailand [25] that found similar association. Furthermore, a study done in northern Ethiopia showed that the risk of vascular complications of type 2 diabetes increased with dyslipidemia [26]. However, studies conducted in Korea [27] and Brazil [28] did not find similar finding and a study done in Saudi Arabia reported dyslipidemia as a protective factor for DR. The association and the inconsistency among findings are not clear and needs further investigation. In addition, those who regularly exercise were found to have lower odds of having DR than those who do not exercise regularly. This finding is supported by studies done in Addis Ababa [11] , Hawassa [29], and Dessie Ethiopia [30] that showed lower odds of having DR among patients with diabetes who had exercise self-care. However, a study done in china did not show a statistically significant association between exercise and diabetic retinopathy [31]. This finding could be related to the increased insulin sensitivity and glucose uptake with regular exercise, which would promote glycemic control. Furthermore, patients who have exercise self-care would have normal body mass index and lower risk of having other diabetic complications. Furthermore, those with poor medication adherence were found to have higher odds of having DR than those with good adherence. This result is supported by a study done in Jimma University Hospital in southwest Ethiopia that reported 3 times higher odds of having DR among those with diabetes who are not adherent to their medication [10]. Poor medication adherence increases the risk of poor glycemic control, which was shown in previous studies as a risk factor for diabetic retinopathy. However, our study did not show a statistically significant association between glycemic control and DR. In addition, having other diabetic complications was also found to increase the odds of having diabetic retinopathy among patients with type 2 diabetes. This finding is comparable with previous studies done Southwest Ethiopia [10], Saudi Arabia [19], Malaysia [17] that showed having a diabetic complication would increase the odds of diabetic retinopathy in patients with type 2 diabetes. Strength and Limitation The study included important self-care, behavioral, and comorbidity characteristics which enabled comprehensive evaluation of factors associated with DR among patients with type 2 diabetes. However, the study was not without limitations. First, glycemic control of patients was assessed using a single fasting blood glucose level. This is unlikely to reflect the long term control and affect the accuracy of glycemic control assessment. Second, the assessment of self-care and behavioral variables like medication adherence, exercise, smoking, and alcohol consumption are self-reported and could be affected by social desirability bias. Conclusion Magnitude of diabetic retinopathy in this study is lower than reported by previous studies done globally and in Ethiopia. Regular screening and follow-up of patients with dyslipidemia and those with other diabetes complications is recommended to detect DR early and prevent vision loss. Furthermore, provision of health education by healthcare professionals regarding regular exercise as well as encouragement of patients to incorporate physical activity into their daily routine is recommended. Public health organizations and policy makers should prioritize initiatives that promote physical activity and healthy lifestyle choices among individuals with diabetes, in order to reduce the risk of diabetic retinopathy. Healthcare providers should also emphasize the importance of medication adherence to patients with type 2 diabetes. Moreover, prompt initiation of management of dyslipidemia and other diabetes complications is recommended. List Of Abbreviations AOR Adjusted Odds Ratio ARTH Asella Referral and Teaching Hospital BMI Body Mass Index DR Diabetic retinopathy DM Diabetes mellitus FBS Fasting blood sugar SPSS Statistical Package for Social Sciences Declarations Ethics approval and consent to participate Ethical approval letter was obtained from Arsi University, Asella referral and teaching hospital Institution Review Board (A/CHS/RC/63/2023). Consent for publication Informed written consent was obtained from the participants Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request Competing interests The authors declare that they have no competing interests Funding The authors received no specific funding for the study Authors' contributions KTB: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Software, Visualization, Writing – original draft. KST: Conceptualization, Data curation, Formal analysis, Methodology, Software, Visualization, Writing and Revision of manuscript. AHR: Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft KWA: Data curation, Formal analysis, Methodology, Software, Visualization, Writing and revising draft. FDC: Methodology, Software, resources, Visualization, revising draft manuscript, NFT: Methodology, Software, resources, Visualization, revising draft manuscript, RTG: Methodology, Software, resources, Visualization, revising draft manuscript, RBT: Methodology, validation, Software, resources, Visualization, revising draft manuscript, RAT: Methodology, Software, validation,Visualization SSS: Methodology, validation, Software, resources, Visualization, revising draft manuscript, TAL: Methodology, Software, resources, Visualization, review and editing manuscript, ZTA: Methodology, resources, Visualization, revising draft manuscript, Acknowledgments We would like to thank Arsi University, College of Health Sciences, for providing us the ethical clearance to conduct the study. We would also like to extend our heartfelt gratitude for Asella Teaching and referral hospital, diabetic follow up clinic staffs that collaborated in the collection of data necessary to prepare this document. References Vujosevic S, Aldington SJ, Silva P, Hernández C, Scanlon P, Peto T, et al. Screening for diabetic retinopathy: new perspectives and challenges. Lancet Diabetes Endocrinol. 2020;8(4):337–47. Teo ZL, Tham Y-C, Yu M, Chee ML, Rim TH, Cheung N, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis. Ophthalmology. 2021;128(11):1580–91. Federation ID. IDF diabetes atlas 10th edition. https://diabetesatlasorg/idfawp/resource-files/2021/07/IDF_Atlas_10th_Edition_2021 . Bekele BB. The prevalence of macro and microvascular complications of DM among patients in Ethiopia 1990–2017: Systematic review. Diabetes metabolic syndrome. 2019;13(1):672–7. Education MH. Harrison's principle of Internal Medicine 21th ed: McGraw Hill Education. Fong DS, Aiello L, Gardner TW, King GL, Blankenship G, Cavallerano JD, et al. Retinopathy in diabetes. Diabetes Care. 2004;27(suppl1):s84–7. Thomas R, Halim S, Gurudas S, Sivaprasad S, Owens D. IDF Diabetes Atlas: A review of studies utilising retinal photography on the global prevalence of diabetes related retinopathy between 2015 and 2018. Diabetes Res Clin Pract. 2019;157:107840. Zegeye AF, Temachu YZ, Mekonnen CK. Prevalence and factors associated with Diabetes retinopathy among type 2 diabetic patients at Northwest Amhara Comprehensive Specialized Hospitals, Northwest Ethiopia 2021. BMC Ophthalmol. 2023;23(1):9. Mersha GA, Alimaw YA, Woredekal AT, Assaye AK, Zeleke TC. Awareness and knowledge of diabetic retinopathy in diabetic patients at a General Hospital in Northwest Ethiopia. SAGE Open Med. 2021;9:20503121211054994. Garoma D, Merga H, Hiko D. Determinants of diabetic retinopathy in Southwest Ethiopia: a facility-based case-control study. BMC Public Health. 2020;20(1):503. Seid K, Tesfaye T, Belay A, Mohammed H. Determinants of diabetic retinopathy in Tikur Anbessa Hospital, Ethiopia: a case-control study. Clin diabetes Endocrinol. 2021;7(1):12. Tsegaw A, Alemu S, Dessie A, Patterson CC, Parry EH, Phillips DI et al. Diabetic retinopathy in type 2 diabetes mellitus patients attending the diabetic clinic of the University of Gondar Hospital, Northwest Ethiopia. Journal of Ophthalmology.2021. Organization WH. Physical status: The use of and interpretation of anthropometry. Report of a WHO Expert Committee: World Health Organization; 1995. Rodriguez-Poncelas A, Miravet-Jiménez S, Casellas A, Barrot-De La Puente JF, Franch-Nadal J, López-Simarro F et al. Prevalence of diabetic retinopathy in individuals with type 2 diabetes who had recorded diabetic retinopathy from retinal photographs in Catalonia (Spain). Br J Ophthalmol. 2015. Bek T, Lund-Andersen H, Hansen AB, Johnsen KB, Sandbæk A, Lauritzen T. The prevalence of diabetic retinopathy in patients with screen-detected type 2 diabetes in Denmark: the ADDITION study. Acta Ophthalmol. 2009;87(3):270–4. Sasongko MB, Widyaputri F, Agni AN, Wardhana FS, Kotha S, Gupta P, et al. Prevalence of Diabetic Retinopathy and Blindness in Indonesian Adults With Type 2 Diabetes. Am J Ophthalmol. 2017;181:79–87. Abougalambou SSI, Abougalambou AS. Risk factors associated with diabetic retinopathy among type 2 diabetes patients at teaching hospital in Malaysia. Diabetes Metabolic Syndrome: Clin Res Reviews. 2015;9(2):98–103. Mohammadi M, Raiegani AAV, Jalali R, Ghobadi A, Salari N. The prevalence of retinopathy among type 2 diabetic patients in Iran: A systematic review and meta-analysis. Reviews Endocr Metabolic Disorders. 2019;20(1):79–88. Al-Rubeaan K, Abu El-Asrar AM, Youssef AM, Subhani SN, Ahmad NA, Al-Sharqawi AH, et al. Diabetic retinopathy and its risk factors in a society with a type 2 diabetes epidemic: a Saudi National Diabetes Registry-based study. Acta Ophthalmol. 2015;93(2):e140–7. Shah S, Feher M, McGovern A, Sherlock J, Whyte MB, Munro N, et al. Diabetic retinopathy in newly diagnosed Type 2 diabetes mellitus: Prevalence and predictors of progression; a national primary network study. Diabetes Res Clin Pract. 2021;175:108776. Hammes H-P, Welp R, Kempe H-P, Wagner C, Siegel E, Holl RW, et al. Risk Factors for Retinopathy and DME in Type 2 Diabetes—Results from the German/Austrian DPV Database. PLoS ONE. 2015;10(7):e0132492. Wang NK, Lai CC, Wang JP, Wu WC, Liu L, Yeh LK, et al. Risk factors associated with the development of retinopathy 10 year after the diagnosis of juvenile-onset type 1 diabetes in Taiwan: a cohort study from the CGJDES. Pediatr Diabetes. 2016;17(6):407–16. Sasso FC, Pafundi PC, Gelso A, Bono V, Costagliola C, Marfella R, et al. High HDL cholesterol: A risk factor for diabetic retinopathy? Findings from NO BLIND study. Diabetes Res Clin Pract. 2019;150:236–44. Liu L, Wu J, Yue S, Geng J, Lian J, Teng W, et al. Incidence Density and Risk Factors of Diabetic Retinopathy Within Type 2 Diabetes: A Five-Year Cohort Study in China (Report 1). Int J Environ Res Public Health. 2015;12(7):7899–909. Euswas N, Phonnopparat N, Morasert K, Thakhampaeng P, Kaewsanit A, Mungthin M, et al. National trends in the prevalence of diabetic retinopathy among Thai patients with type 2 diabetes and its associated factors from 2014 to 2018. PLoS ONE. 2021;16(1):e0245801. Wolde HF, Atsedeweyen A, Jember A, Awoke T, Mequanent M, Tsegaye AT, et al. Predictors of vascular complications among type 2 diabetes mellitus patients at University of Gondar Referral Hospital: a retrospective follow-up study. BMC Endocr Disorders. 2018;18(1):52. Kim YJ, Kim J-G, Lee JY, Lee KS, Joe SG, Park J-Y, et al. Development and Progression of Diabetic Retinopathy and Associated Risk Factors in Korean Patients with Type 2 Diabetes: The Experience of a Tertiary Center. J Korean Med Sci. 2014;29(12):1699–705. Lima VC, Cavalieri GC, Lima MC, Nazario NO, Lima GC. Risk factors for diabetic retinopathy: a case–control study. Int J Retina Vitreous. 2016;2(1):21. Alemayehu HB, Tegegn MT, Tilahun MM. Prevalence and associated factors of visual impairment among adult diabetic patients visiting Adare General Hospital, Hawassa, South Ethiopia, 2022. PLoS ONE. 2022;17(10):e0276194. Seid MA, Ambelu A, Diress M, Yeshaw Y, Akalu Y, Dagnew B. Visual impairment and its predictors among people living with type 2 diabetes mellitus at Dessie town hospitals, Northeast Ethiopia: institution-based cross-sectional study. BMC Ophthalmol. 2022;22(1):52. Yan ZP, Ma JX. Risk factors for diabetic retinopathy in northern Chinese patients with type 2 diabetes mellitus. Int J Ophthalmol. 2016;9(8):1194–9. Additional Declarations No competing interests reported. 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University","correspondingAuthor":false,"prefix":"","firstName":"Koricho","middleName":"Simie","lastName":"Tolla","suffix":""},{"id":288443818,"identity":"479453a8-9f10-4484-aab3-2377987b1aa6","order_by":2,"name":"Ashenafi Habtamu Regesu","email":"","orcid":"","institution":"Arsi University","correspondingAuthor":false,"prefix":"","firstName":"Ashenafi","middleName":"Habtamu","lastName":"Regesu","suffix":""},{"id":288443819,"identity":"2a4b72f7-4faa-4aec-b6a5-f17fb8bdc6e8","order_by":3,"name":"Kibruyisfaw Weldeab Abore","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBACg8M8IEqCgb0BSH0AYjZ2AloMm6FaeA4wMDDOAGlhJqDFmIEHwgBpYQazCWkxY+c99pm3zcKeh/2MmbTNr23yfMwMjB8+5uDWYsPMlzybt00isYcnx0w6t++2YRszA7PkzG34tPAYMwO1JNgz5G67ndtzmxGohY2ZF48WM6gWex7+t9tuW/bctieoxRiqhbFHAmgLw4/biQS1GDbzJTPOOQf0i8T77z97G24ntzEzNuP1i8H5s4cZ3pTVAR2Wlmzw489t2/ntzQc/fMSjBQwY2WCMNjDZQEA9CPzBYIyCUTAKRsEoQAAA0iNH49KNF9EAAAAASUVORK5CYII=","orcid":"","institution":"St. Paul’s Hospital Millennium Medical College","correspondingAuthor":true,"prefix":"","firstName":"Kibruyisfaw","middleName":"Weldeab","lastName":"Abore","suffix":""},{"id":288443820,"identity":"7d58e665-bc6e-4143-93b6-a0987bd92691","order_by":4,"name":"Fraol Daba Chinkey","email":"","orcid":"","institution":"Ambo University Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fraol","middleName":"Daba","lastName":"Chinkey","suffix":""},{"id":288443821,"identity":"4ab2e2f0-4b37-4588-9d5a-6c9eae93486b","order_by":5,"name":"Natnael Fikadu Tekle","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Natnael","middleName":"Fikadu","lastName":"Tekle","suffix":""},{"id":288443822,"identity":"d5f08449-0683-4f3a-8705-ef204a3d14da","order_by":6,"name":"Rekik Teshale Gebre","email":"","orcid":"","institution":"Menilik II Medical and Health Science College","correspondingAuthor":false,"prefix":"","firstName":"Rekik","middleName":"Teshale","lastName":"Gebre","suffix":""},{"id":288443823,"identity":"946be954-51fe-4b05-b46f-02f3198f94f9","order_by":7,"name":"Robel Bayou Tilahun","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Robel","middleName":"Bayou","lastName":"Tilahun","suffix":""},{"id":288443824,"identity":"087f031a-1f9b-474c-99e8-fbc8193d9ce6","order_by":8,"name":"Rediet Atnafu Tilahun","email":"","orcid":"","institution":"Zewditu Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rediet","middleName":"Atnafu","lastName":"Tilahun","suffix":""},{"id":288443825,"identity":"a7c9b134-efcf-4773-8102-c2249df714fe","order_by":9,"name":"Sofonyas Silesh Sinshaw","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Sofonyas","middleName":"Silesh","lastName":"Sinshaw","suffix":""},{"id":288443826,"identity":"448ec958-55e8-4225-8e2e-c31c7f800874","order_by":10,"name":"Tsion Andrias Lechebo","email":"","orcid":"","institution":"The Ethiopian private Hospitals and Centers Association","correspondingAuthor":false,"prefix":"","firstName":"Tsion","middleName":"Andrias","lastName":"Lechebo","suffix":""},{"id":288443827,"identity":"4fb41c0f-cb55-46d9-9260-9177251fe261","order_by":11,"name":"Zekarias Tadele Alemneh","email":"","orcid":"","institution":"Aksum University Comprehensive Specialized Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zekarias","middleName":"Tadele","lastName":"Alemneh","suffix":""}],"badges":[],"createdAt":"2024-04-07 12:50:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4231400/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4231400/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54316075,"identity":"3e625adb-ff5c-4ecf-ab6f-a2c7e382670c","added_by":"auto","created_at":"2024-04-08 17:47:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26909,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMagnitude of diabetic retinopathy among type 2 diabetes patient in ASTRH, 2023\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4231400/v1/11490f71af471d84524c7c04.png"},{"id":62378564,"identity":"445614e3-9319-459d-a77c-729d9d90f25c","added_by":"auto","created_at":"2024-08-13 13:44:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1195026,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4231400/v1/db23e893-b638-4863-8c7c-ff1a896f1214.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Magnitude of diabetic retinopathy and its associated factors among patients with type 2 diabetes on follow up at diabetes clinic of Asella Referral and Teaching Hospital, Asella, Ethiopia: a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetes mellitus (DM) refers to a group of common metabolic disorders that share the phenotype of hyperglycemia and affected more than half a billion people globally in 2021 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In Ethiopia, the prevalence of Diabetes was estimated to be more than 5\u0026nbsp;million, which is among the highest in Africa, and with a concomitant high burden of undiagnosed DM [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiabetic retinopathy is a microvascular complication of diabetes and is a major cause of impaired vision in patients aged between 20 and 74 years [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The International Diabetes Federation (IDF) estimated that more than 100\u0026nbsp;million people worldwide had DR in 2020, and is expected to show more than 50% rise by the year 2045 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Of the global burden of DR, African countries such as Ethiopia take the highest share [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This estimate is significant considering the high prevalence of undiagnosed diabetic population in the continent [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies conducted in Ethiopia among diabetic patients, both type 1 and type 2, reported magnitudes of DR ranging from 4.7\u0026ndash;36.3% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, this figure could be under representative given the high burden of undiagnosed diabetes, the poor health care seeking behavior, and the low diabetic retinopathy awareness [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In Ethiopia, the majority of diabetic patients are Type 2 DM [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. literature show that more than two-thirds of patients with type 2 diabetes develop diabetic retinopathy in the first two decades of being diagnosed with DM [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, to the best of the authors\u0026rsquo; knowledge, only one study examined risk factors for DR among patients with Type 2 DM in Ethiopia [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, previous studies did not include important self-care and behavioral variables such as medication adherence, alcohol consumption, and exercise, as well as important comorbidity profiles such as dyslipidemia and proteinuria.\u003c/p\u003e \u003cp\u003eThus, this study would add to the limited body of evidence available on DR among type 2 diabetes patients in Ethiopia and could serve as a baseline study up on which other researchers interested in the area can broaden. Therefore, this study aimed to assess the magnitude of diabetic retinopathy and its associated factor among patients with type 2 diabetes on follow-up at diabetic clinic of Asella Referral and Teaching Hospital.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting and study design\u003c/h2\u003e \u003cp\u003eThe study was conducted at Arsi University, College of Health Science, Asella Teaching and Referral Hospital, located 175 kilometers from Addis Ababa, the capital of Ethiopia. The hospital is the largest hospital in the area serving more than 3.5\u0026nbsp;million populations. The chronic follow-up clinic at ARTH serves a total of 2800 Type 2 diabetic patients and is staffed with 1 internist, 2 internal medicine residents covering the clinic with rotation, and 4 nurses. There are also 3 ophthalmologists, 3 optometrists, and 4 ophthalmology nurses in the hospital. An institution-based cross-sectional study design was conducted from 11-December-2023 to 12-January-2024.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSource and study population\u003c/h2\u003e \u003cp\u003eAdult type 2 diabetes patients on follow-up at ARTH DM clinic were the source population, while all adult type 2 diabetes patients on follow-up at ARTH DM clinic who visited during the study period were the study population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eAll adults with type 2 diabetes mellitus aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years old who are on follow-up were eligible for this study. Patients who were critically ill and unable to communicate during data collection and those patients with other types of DM such as Type 1 and gestational diabetes were excluded from the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSample size and sampling procedure\u003c/h2\u003e \u003cp\u003eThe sample size was determined using Epi info version 7 using the following assumptions: 80% power, proportion of DR among those without hypertension\u0026thinsp;=\u0026thinsp;0.065 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], AOR\u0026thinsp;=\u0026thinsp;2.56 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], 95% confidence interval, and total population(N)\u0026thinsp;=\u0026thinsp;2800. A final sample size of 428 was achieved after accounting for finite population correction and 10% non-response. A systematic random sampling technique using a sampling interval of 7 was used to select study subjects. When patients do not meet the inclusion criteria, the next patient who meets the criteria was included. Patients\u0026rsquo; medical record number were listed accordingly and marked to avoid patient selection more than once.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData collection technique and quality control\u003c/h2\u003e \u003cp\u003eData were collected through chart review using a checklist, an interviewer administered questionnaire. The tool was translated into the local Afaan oromo language and back translated to English to check for consistency. The data collection tool was pretested on 5% of the sample size at Adama Hospital medical college. Based on the pretest findings, corrections were made to the data extraction form. Data collection was done by two trained nurses and the supervision of the overall data collection activity of the study was carried out by the investigators. Prior to the data collection, a one day orientation was given to the data collectors. The completed data were randomly selected and cross-checked with medical records of the patients to ensure consistency at the end of each data collection day.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eOperational definition\u003c/h2\u003e \u003cp\u003eDiabetic retinopathy: Retinopathy was determined by the evaluating ophthalmologist after slit lamp biomicroscopy evaluation. Evaluation by an ophthalmologist was performed using a slit lamp microscopy and a 90D condensing lens after dilating the pupil with 1% Tropicamide.\u003c/p\u003e \u003cp\u003eMedication adherence: adherence was assessed using the Morisky Medication Adherence Scale (MMAS-4). For the four questions, \u0026lsquo;yes\u0026rsquo; was scored as 0 and \u0026lsquo;no\u0026rsquo; was scored as 1. The total score was found after summing these scores and labeled as poor if the total score is 1, moderate\u0026thinsp;=\u0026thinsp;2\u0026ndash;3, and good if scored 4. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Moderate and poor were categorized as poor for the logistic regression analysis.\u003c/p\u003e \u003cp\u003eGlycemic control: Fasting blood sugar (FBS) measurement 70\u0026ndash;126 mg/dl on the last follow up was used to label as having good glycemic control. Those with FBS\u0026thinsp;\u0026lt;\u0026thinsp;70 mg/dl and \u0026gt;\u0026thinsp;126 mg/dl were labeled as having poor glycemic control [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHypertension control: a systolic blood pressure\u0026thinsp;\u0026lt;\u0026thinsp;140 mm hg and diastolic blood pressure\u0026thinsp;\u0026lt;\u0026thinsp;90 mm hg on the last visit was considered as controlled hypertension. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eDyslipidemia: total cholesterol\u0026thinsp;\u0026gt;\u0026thinsp;200 mg/dl and/or high density lipoprotein level\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dl for male\u0026thinsp;\u0026lt;\u0026thinsp;50 mg/dl for females and and/or low density lipoprotein level\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/dl and/or triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dl. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eObesity: Body Mass Index (BMI) was classified as underweight (\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), normal (18.5 kg/m\u003csup\u003e2\u003c/sup\u003e-24.9 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e), and obese (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlcohol consumption: reported consumption of alcohol consumption with in the 6 months prior to data collection.\u003c/p\u003e \u003cp\u003eRegular exercise: 30 minute vigorous intensity physical activity for 3 days or more per week apart from routine work related activity was labeled as regular exercise [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData processing and analysis\u003c/h2\u003e \u003cp\u003eData entry, coding and verification were done using Epi-data 3.1 and data analysis was performed using SPSS version 26. Descriptive analysis was performed to describe the socio-demographic characteristics and clinical profile of the participants. Categorical variables were summarized using frequency and percentage, while continuous variables were summarized using mean and standard deviation. Binary logistic regression and multivariable logistic regression were performed to determine associated factors of diabetic retinopathy among Type 2 diabetic patients, using α\u0026thinsp;=\u0026thinsp;0.05 as significance level. Those variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 were considered for multivariable logistic regression. The association was measured using adjusted odds ratio (AOR) with the corresponding confidence interval. Association with p-value less than 0.05 were considered significant. Model goodness of fit was assessed using Hosmer and Lemshow goodness of fit test.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eEthical statement\u003c/h2\u003e\n\u003cp\u003eEthical approval letter was obtained from Arsi University, Asella referral and teaching hospital Institution Review Board (A/CHS/RC/63/2023). A formal letter from the department of medicine was sent to the participating departments. Informed written consent was obtained from the participants. The retrieved data were kept strictly confidential and the names of the patients were not included in the study.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSocio-demographic characteristics\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, more than a third (38.1%) of the participants were in the age group of 45-55 years. Moreover, more than half of the respondents were male (56.3%) and more than two-third (73.6%) of the participants were married. Regarding educational status, 104 (24.3%) had secondary education and 70 (16.4%) had college or higher education. Based on occupation, 105 (24.5%) were farmers and 96 (22.4%) were house wives. \u0026nbsp; Furthermore, the majority of the participants (66.1%) were from an urban area. Additionally, more than a third of the participants (34.6%) had income \u0026ge;7500 Ethiopian birr. (\u003cstrong\u003eTable 1\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Socio-demographic profile of patients with Type 2 diabetes at ASRTH, 2023\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"653\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.58192955589587%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.987748851454825%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency \u0026nbsp;(N=428)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02297090352221%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e45-55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e56-65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e29.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02297090352221%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSex\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e56.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e43.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02297090352221%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e73.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDivorced\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eWidowed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02297090352221%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eEducation status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUnable to read and write\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRead and write\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eprimary school\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSecondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCollege and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02297090352221%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eOccupation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eGovernment employee\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNon-governmental employee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMerchant\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFarmer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRetired\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHousewife\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\n \u003cp\u003eIncome\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1500-3499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3500-5499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e35.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e5500-7499\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;7500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e34.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\n \u003cp\u003eResidence\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUrban\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e66.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.77182235834609%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRural\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.546707503828483%\" valign=\"top\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.43032159264931%\" valign=\"top\"\u003e\n \u003cp\u003e33.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eOthers; daily laborers, priests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComorbidity profile of participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the participants, 160 (37.4%) have hypertension, and of those who have hypertension 86 (53.8%) have controlled hypertension. Around 175 (41%) patients have dyslipidemia and 293 (68.5%) are on statin treatment. Regarding BMI, more than one-third (164; 38.3%) were overweight and 43 (10%) participants were obese. Moreover, 60 (14%) participants had + 2 or more proteinuria and the mean creatinine was 0.849 (SD: \u0026plusmn;0.362). (\u003cstrong\u003eTable 2\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"662\" height=\"697\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-care and behavioral profile of patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt was found that 37 (8.6%) of the participants had history of ever smoking, while only 4 of the participants were current smokers. Meanwhile, 47 (11%) of the respondents consumed alcohol in the past 6 months and the majority of them consumed beer (22; 46.8%) and Tella (19; 40.4%). Of the respondents, 161(37.6%) have a glucometer at home and 362 (84.6%) have received diabetes education. Regarding medication adherence, 326 (76.2%) have good medication adherence while only 5 (1.2%) have poor adherence. (\u003cstrong\u003eTable 3\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. \u0026nbsp;Self-care and behavioral profile of patients with type 2 diabetes on follow-up at ASRTH, 2023\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"647\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.31375579598145%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFactors\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.31375579598145%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eEver smoked\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.45595054095827%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85780525502319%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.413162705667276%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.787934186471663%\" valign=\"top\"\u003e\n \u003cp\u003e391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.79890310786106%\" valign=\"top\"\u003e\n \u003cp\u003e91.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.31375579598145%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRegular exercise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.45595054095827%\" rowspan=\"2\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85780525502319%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.413162705667276%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.787934186471663%\" valign=\"top\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.79890310786106%\" valign=\"top\"\u003e\n \u003cp\u003e86.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.31375579598145%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePast 6 month Alcohol consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.45595054095827%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85780525502319%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.45595054095827%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85780525502319%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.31375579598145%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eType of alcohol consumed (n=47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.45595054095827%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85780525502319%\" valign=\"top\"\u003e\n \u003cp\u003eLocal areke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.45595054095827%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85780525502319%\" valign=\"top\"\u003e\n \u003cp\u003eTella\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\n \u003cp\u003e40.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.45595054095827%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85780525502319%\" valign=\"top\"\u003e\n \u003cp\u003eBeer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\n \u003cp\u003e46.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.31375579598145%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHave a glucometer at home\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.45595054095827%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85780525502319%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.413162705667276%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.787934186471663%\" valign=\"top\"\u003e\n \u003cp\u003e267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.79890310786106%\" valign=\"top\"\u003e\n \u003cp\u003e62.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.31375579598145%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eReceived diabetic health education\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.45595054095827%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85780525502319%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\n \u003cp\u003e362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\n \u003cp\u003e84.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.413162705667276%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.787934186471663%\" valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.79890310786106%\" valign=\"top\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.31375579598145%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMedication adherence\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.45595054095827%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.85780525502319%\" valign=\"top\"\u003e\n \u003cp\u003eGood\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.493044822256568%\" valign=\"top\"\u003e\n \u003cp\u003e326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.19319938176198%\" valign=\"top\"\u003e\n \u003cp\u003e76.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.413162705667276%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.787934186471663%\" valign=\"top\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.79890310786106%\" valign=\"top\"\u003e\n \u003cp\u003e22.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.413162705667276%\" valign=\"top\"\u003e\n \u003cp\u003ePoor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.787934186471663%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.79890310786106%\" valign=\"top\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiabetic profile of patients\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMore than two-third of the participants in this study were diagnosed with type 2 diabetes less than 10 year back. Of the respondents, 296 (69.2%) had poor glycemic control. In addition, nearly half of them were on oral glucose lowering agents (213; 49.8%) and had one or more diabetes complication (211; 49.3%). Diabetic neuropathy (83; 39.3%) was the most common complication followed by cardiovascular complications (61; 28.9%) (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Diabetes related profile of type 2 diabetes patients on follow up at ASTRH, 202\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.561922365988906%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003eFrequency\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003ePercentage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.561922365988906%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDuration of diabetes (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003e1-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003e5-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e36.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.561922365988906%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eGlycemic control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003eGood\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e30.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003epoor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e69.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.561922365988906%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eType of medication\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003eOral\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e49.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003eInsulin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003eMixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.561922365988906%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes related complications\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e49.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e50.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.561922365988906%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eType of DM complication other than DR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003eCardiovascular\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003eChronic kidney disease\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003eNeuropathy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e39.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003eCerebrovascular\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003ePAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetic foot complication\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.41404805914972%\" valign=\"top\"\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.147874306839185%\" valign=\"top\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOthers; erectile dysfunction, cataract PAD; peripheral arterial disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMagnitude of Diabetic retinopathy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the participants, 53(12.4%) had diabetic retinopathy (95% CI: 9.4, 15.9).\u0026nbsp;(\u003cstrong\u003eFig 1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig 1: Magnitude of diabetic retinopathy among type 2 diabetes patient in ASTRH, 2023\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors associated with DR among patients with type 2 diabetes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter computing bivariate binary logistic regression, sex, age, marital status, smoking, alcohol consumption, exercise, medication adherence, glycemic control, mode of treatment, BMI, proteinuria, presence of diabetic complications, hypertension, dyslipidemia, and statin use were found to be candidate variables (p- value \u0026lt;0.25) for multivariable binary logistic regression. Subsequently, Multivariable logistic regression was performed and it was found that, after adjusting for other variables, dyslipidemia, exercise, medication adherence, and the presence of other DM complications were statistically significant predictors of diabetic retinopathy among patients with type 2 diabetes.\u003c/p\u003e\n\u003cp\u003eParticipants with dyslipidemia had 2.538 times higher odds of having DR than those without dyslipidemia (AOR=2.538, 95% CI: 1.190, 5.412).\u0026nbsp;Meanwhile, those who exercise regularly had 95.2% lower odds of having DR compared to those who do not (AOR=0.048, 95%: 0.004, 0.565). Moreover, those patients who had poor adherence to medication had 3.7 times higher odds of having DR than those who have good adherence (AOR=3.706, 95% CI: 1.730, 7.940). Furthermore, those who have diabetes complication other than DR have 2.54 times higher odds of having DR compared to those who do not (AOR=2.537, 95% CI: 1.10, 5.850). (\u003cstrong\u003eTable 5\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Bivariate and multivariable analysis of factors associated with diabetic retinopathy among patients with type 2 diabetes on follow-up at ASRTH, 2023\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"692\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.23121387283237%\" colspan=\"2\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.734104046242773%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDiabetic retinopathy \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCOR (95%C.I)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.648648648648646%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.351351351351354%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAge group\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.708513708513708%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.366522366522368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49062049062049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"top\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e11(15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e60(84.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003e45-55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e13(8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e150(92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e0.473(0.201, 1.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e0.257(0.085,0.772)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003e56-65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e22(17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e105(82.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1.143(0.519, 2.519)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e0.524(0.181,1.516)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e7(10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e60(89.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e0.636( 0.231, 1.752)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e0.278(0.069, 1.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSex\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.708513708513708%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.366522366522368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49062049062049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e37(15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e204(84.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1.938(1.042,1.606)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1.442(0.675,3.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e16(8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e171(91.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.708513708513708%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.366522366522368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49062049062049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"top\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e10(21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e36(78.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1.389(0.510,3.781)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1.526(0.388,5.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e33(10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e282(89.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e0.585(0.263,1.304)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e0.56(0.196,1.597)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eWidowed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e1(7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e12(92.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e0.417(0.048,3.628)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e0.384(0.033,4.478)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eDivorced\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e9(16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e45(83.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.708513708513708%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.366522366522368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49062049062049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e30(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e130(81.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e2.458(1.372,4.405)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e2.036(0.95,4.365)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e23(8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e36(91.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDyslipidemia\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.708513708513708%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.366522366522368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49062049062049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e34(19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e141(80.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e2.97(1.631,5.406)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e2.538(1.190, 5.412)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eNo \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e19(7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e234(92.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eProteinuria \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.708513708513708%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.366522366522368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49062049062049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"top\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eNormal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e24(9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e225(90.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eTrace\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e4(6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e60(93.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0.625(0.209,1.870)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0.622(0.260,1.484)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003e+1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e9(16.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e46(83.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1.834(0.801,4.203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e0.213(0.053,0.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;+2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e16(26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e44(73.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e3.409(1.676,6.936)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1.166(0.376, 3.622)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.708513708513708%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.366522366522368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49062049062049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"top\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eUnderweight and normal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e25(11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e196(88.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eOverweight\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e18(11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e146(89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0.967 (0.172,0.962)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0.62(0.277,1.358)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eObese\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e10(23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e33(76.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e2.376(1.045, 5.399)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e2.323(0.839,6.434)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eStatin use\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.708513708513708%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.366522366522368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49062049062049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e31(10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e262(89.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e0.608(0.337,1.096)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1.219(0.544,2.732)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eNo \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e22(16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e113(83.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSmoking \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.708513708513708%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.366522366522368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49062049062049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e9(24.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e28(75.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e2.535(1.123,5.720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1.836(0.622,5.420)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e44(11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e75(88.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAlcohol consumption\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.708513708513708%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.366522366522368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49062049062049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e12(22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e35(77.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e2.843(1.368,5.908)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1.850(0.648,5.282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eNo \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e41(10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e340(89.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.76923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExercise \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.708513708513708%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.366522366522368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49062049062049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e1(1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e56(98.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e0.11 (0.015,0.809)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e0.048(0.004,0.565)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e52(14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e319(86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdherence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.708513708513708%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.366522366522368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49062049062049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eGood\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e40(50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e40(50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003ePoor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e137(62.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e81(37.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e3.455(1.907,6.263)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e3.706(1.730,7.940)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eGlycemic control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.708513708513708%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.366522366522368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49062049062049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eGood\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e28(8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e298(91.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e0.482(0.234,0.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e0.899(0.365,2.212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003ePoor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e25(24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e77(75.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMode of treatment \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.708513708513708%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.366522366522368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49062049062049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eInsulin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e22(18.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e98(81.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1.553(0.725, 3.326)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1.5(0.564,3.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eOral and insulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e12(12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e83(87.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eOral\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e19(8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e194(91.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e0.677(0.315, 1.459)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e0.568(0.228,1.414)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetic complication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.708513708513708%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.366522366522368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.49062049062049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e36(17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e175(82.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e2.420(1.313,4.460)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e2.537(1.10,5.850)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.069364161849711%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.16184971098266%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.00578034682081%\" valign=\"top\"\u003e\n \u003cp\u003e17(7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.728323699421965%\" valign=\"top\"\u003e\n \u003cp\u003e200(92.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.398843930635838%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.520231213872833%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.115606936416185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study assessed the magnitude of diabetic retinopathy and its associated factors among 428 patients with type 2 diabetes on follow-up at ASRTH diabetic clinic. The magnitude of diabetic retinopathy was found to be 12.4%. This finding is comparable with a study conducted in Spain that reported 12.3% DR\u0026nbsp;[14]. However, it was higher than studies done in Denmark (6.8%)\u0026nbsp;[15]\u0026nbsp;and lower than studies done in Northwest Ethiopia (36.3%)\u0026nbsp;[8], Northern Ethiopia (16%)\u0026nbsp;[12], Indonesia (43.1%)\u0026nbsp;[16], Malaysia (39.3%)\u0026nbsp;[17], Iran (37.8%)\u0026nbsp;[18], Saudi Arabia (19.7%)\u0026nbsp;[19], and United Kingdom (18%)\u0026nbsp;[20]. This difference in the prevalence of diabetic retinopathy could be attributed to differences in sample size, baseline comorbidity status, and other diabetic complications.\u003c/p\u003e\n\u003cp\u003ePatients with type 2 diabetes who had dyslipidemia were also found to have higher odds of diabetic retinopathy compared to their counterparts. This finding is supported by studies done in Germany\u0026nbsp;[21], Taiwan\u0026nbsp;[22], Malaysia\u0026nbsp;[17], Italy\u0026nbsp;[23], China\u0026nbsp;[24], and Thailand\u0026nbsp;[25]\u0026nbsp;that found similar association. Furthermore, a study done in northern Ethiopia showed that the risk of vascular complications of type 2 diabetes increased with dyslipidemia\u0026nbsp;[26]. However, studies conducted in Korea\u0026nbsp;[27]\u0026nbsp;and Brazil\u0026nbsp;[28]\u0026nbsp;did not find similar finding and a study done in Saudi Arabia reported dyslipidemia as a protective factor for DR. \u0026nbsp;The association and the inconsistency among findings are not clear and needs further investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, those who regularly exercise were found to have lower odds of having DR than those who do not exercise regularly. This finding is supported by studies done in Addis Ababa\u0026nbsp;[11]\u0026nbsp;, Hawassa \u0026nbsp;\u0026nbsp;[29], and Dessie Ethiopia\u0026nbsp;[30]\u0026nbsp;that showed lower odds of having DR among patients with diabetes who had exercise self-care. However, a study done in china did not show a statistically significant association between exercise and diabetic retinopathy\u0026nbsp;[31]. This finding could be related to the increased insulin sensitivity and glucose uptake with regular exercise, which would promote glycemic control. Furthermore, patients who have exercise self-care would have normal body mass index and lower risk of having other diabetic complications.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, those with poor medication adherence were found to have higher odds of having DR than those with good adherence. This result is supported by a study done in Jimma University Hospital in southwest Ethiopia that reported 3 times higher odds of having DR among those with diabetes who are not adherent to their medication\u0026nbsp;[10]. Poor medication adherence increases the risk of poor glycemic control, which was shown in previous studies as a risk factor for diabetic retinopathy. However, our study did not show a statistically significant association between glycemic control and DR. In addition, having other diabetic complications was also found to increase the odds of having diabetic retinopathy among patients with type 2 diabetes. This finding is comparable with previous studies done Southwest Ethiopia\u0026nbsp;[10], Saudi Arabia\u0026nbsp;[19], Malaysia\u0026nbsp;[17]\u0026nbsp;that showed having a diabetic complication would increase the odds of diabetic retinopathy in patients with type 2 diabetes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrength and Limitation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included important self-care, behavioral, and comorbidity characteristics which enabled comprehensive evaluation of factors associated with DR among patients with type 2 diabetes. However, the study was not without limitations. \u0026nbsp; First, glycemic control of patients was assessed using a single fasting blood glucose level. This is unlikely to reflect the long term control and affect the accuracy of glycemic control assessment. Second, the assessment of self-care and behavioral variables like medication adherence, exercise, smoking, and alcohol consumption are self-reported and could be affected by social desirability bias.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMagnitude of diabetic retinopathy in this study is lower than reported by previous studies done globally and in Ethiopia. Regular screening and follow-up of patients with dyslipidemia and those with other diabetes complications is recommended to detect DR early and prevent vision loss. Furthermore, provision of health education by healthcare professionals regarding regular exercise as well as encouragement of patients to incorporate physical activity into their daily routine is recommended. Public health organizations and policy makers should prioritize initiatives that promote physical activity and healthy lifestyle choices among individuals with diabetes, in order to reduce the risk of diabetic retinopathy. Healthcare providers should also emphasize the importance of medication adherence to patients with type 2 diabetes. Moreover, prompt initiation of management of dyslipidemia and other diabetes complications is recommended.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARTH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAsella Referral and Teaching Hospital\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiabetic retinopathy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFasting blood sugar\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval letter was obtained from Arsi University, Asella referral and teaching hospital Institution Review Board (A/CHS/RC/63/2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed written consent was obtained from the participants\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKTB: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Software, Visualization, Writing \u0026ndash; original draft. KST: Conceptualization, Data curation, Formal analysis, Methodology, Software, Visualization, Writing and Revision of manuscript. AHR: Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing \u0026ndash; original draft \u0026nbsp; KWA: Data curation, Formal analysis, Methodology, Software, Visualization, Writing and revising draft. FDC: Methodology, Software, resources, Visualization, revising draft manuscript, NFT: Methodology, Software, resources, Visualization, revising draft manuscript, RTG: Methodology, Software, resources, Visualization, revising draft manuscript, RBT: Methodology, validation, Software, resources, Visualization, revising draft manuscript, RAT: Methodology, Software, validation,Visualization SSS: Methodology, validation, Software, resources, Visualization, revising draft manuscript, TAL: Methodology, Software, resources, Visualization, review and editing manuscript, ZTA: Methodology, resources, Visualization, revising draft manuscript,\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Arsi University, College of Health Sciences, for providing us the ethical clearance to conduct the study. We would also like to extend our heartfelt gratitude for Asella Teaching and referral hospital, diabetic follow up clinic staffs that collaborated in the collection of data necessary to prepare this document.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVujosevic S, Aldington SJ, Silva P, Hern\u0026aacute;ndez C, Scanlon P, Peto T, et al. Screening for diabetic retinopathy: new perspectives and challenges. Lancet Diabetes Endocrinol. 2020;8(4):337\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeo ZL, Tham Y-C, Yu M, Chee ML, Rim TH, Cheung N, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis. Ophthalmology. 2021;128(11):1580\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFederation ID. IDF diabetes atlas 10th edition. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://diabetesatlasorg/idfawp/resource-files/2021/07/IDF_Atlas_10th_Edition_2021\u003c/span\u003e\u003cspan address=\"https://diabetesatlasorg/idfawp/resource-files/2021/07/IDF_Atlas_10th_Edition_2021\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBekele BB. The prevalence of macro and microvascular complications of DM among patients in Ethiopia 1990\u0026ndash;2017: Systematic review. Diabetes metabolic syndrome. 2019;13(1):672\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEducation MH. Harrison's principle of Internal Medicine 21th ed: McGraw Hill Education.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFong DS, Aiello L, Gardner TW, King GL, Blankenship G, Cavallerano JD, et al. Retinopathy in diabetes. Diabetes Care. 2004;27(suppl1):s84\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas R, Halim S, Gurudas S, Sivaprasad S, Owens D. IDF Diabetes Atlas: A review of studies utilising retinal photography on the global prevalence of diabetes related retinopathy between 2015 and 2018. Diabetes Res Clin Pract. 2019;157:107840.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZegeye AF, Temachu YZ, Mekonnen CK. Prevalence and factors associated with Diabetes retinopathy among type 2 diabetic patients at Northwest Amhara Comprehensive Specialized Hospitals, Northwest Ethiopia 2021. BMC Ophthalmol. 2023;23(1):9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMersha GA, Alimaw YA, Woredekal AT, Assaye AK, Zeleke TC. Awareness and knowledge of diabetic retinopathy in diabetic patients at a General Hospital in Northwest Ethiopia. SAGE Open Med. 2021;9:20503121211054994.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaroma D, Merga H, Hiko D. Determinants of diabetic retinopathy in Southwest Ethiopia: a facility-based case-control study. BMC Public Health. 2020;20(1):503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeid K, Tesfaye T, Belay A, Mohammed H. Determinants of diabetic retinopathy in Tikur Anbessa Hospital, Ethiopia: a case-control study. Clin diabetes Endocrinol. 2021;7(1):12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsegaw A, Alemu S, Dessie A, Patterson CC, Parry EH, Phillips DI et al. Diabetic retinopathy in type 2 diabetes mellitus patients attending the diabetic clinic of the University of Gondar Hospital, Northwest Ethiopia. Journal of Ophthalmology.2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH. Physical status: The use of and interpretation of anthropometry. Report of a WHO Expert Committee: World Health Organization; 1995.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodriguez-Poncelas A, Miravet-Jim\u0026eacute;nez S, Casellas A, Barrot-De La Puente JF, Franch-Nadal J, L\u0026oacute;pez-Simarro F et al. Prevalence of diabetic retinopathy in individuals with type 2 diabetes who had recorded diabetic retinopathy from retinal photographs in Catalonia (Spain). Br J Ophthalmol. 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBek T, Lund-Andersen H, Hansen AB, Johnsen KB, Sandb\u0026aelig;k A, Lauritzen T. The prevalence of diabetic retinopathy in patients with screen-detected type 2 diabetes in Denmark: the ADDITION study. Acta Ophthalmol. 2009;87(3):270\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasongko MB, Widyaputri F, Agni AN, Wardhana FS, Kotha S, Gupta P, et al. Prevalence of Diabetic Retinopathy and Blindness in Indonesian Adults With Type 2 Diabetes. Am J Ophthalmol. 2017;181:79\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbougalambou SSI, Abougalambou AS. Risk factors associated with diabetic retinopathy among type 2 diabetes patients at teaching hospital in Malaysia. Diabetes Metabolic Syndrome: Clin Res Reviews. 2015;9(2):98\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammadi M, Raiegani AAV, Jalali R, Ghobadi A, Salari N. The prevalence of retinopathy among type 2 diabetic patients in Iran: A systematic review and meta-analysis. Reviews Endocr Metabolic Disorders. 2019;20(1):79\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Rubeaan K, Abu El-Asrar AM, Youssef AM, Subhani SN, Ahmad NA, Al-Sharqawi AH, et al. Diabetic retinopathy and its risk factors in a society with a type 2 diabetes epidemic: a Saudi National Diabetes Registry-based study. Acta Ophthalmol. 2015;93(2):e140\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah S, Feher M, McGovern A, Sherlock J, Whyte MB, Munro N, et al. Diabetic retinopathy in newly diagnosed Type 2 diabetes mellitus: Prevalence and predictors of progression; a national primary network study. Diabetes Res Clin Pract. 2021;175:108776.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHammes H-P, Welp R, Kempe H-P, Wagner C, Siegel E, Holl RW, et al. Risk Factors for Retinopathy and DME in Type 2 Diabetes\u0026mdash;Results from the German/Austrian DPV Database. PLoS ONE. 2015;10(7):e0132492.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang NK, Lai CC, Wang JP, Wu WC, Liu L, Yeh LK, et al. Risk factors associated with the development of retinopathy 10 year after the diagnosis of juvenile-onset type 1 diabetes in Taiwan: a cohort study from the CGJDES. Pediatr Diabetes. 2016;17(6):407\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasso FC, Pafundi PC, Gelso A, Bono V, Costagliola C, Marfella R, et al. High HDL cholesterol: A risk factor for diabetic retinopathy? Findings from NO BLIND study. Diabetes Res Clin Pract. 2019;150:236\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu L, Wu J, Yue S, Geng J, Lian J, Teng W, et al. Incidence Density and Risk Factors of 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\u0026ndash;909.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuswas N, Phonnopparat N, Morasert K, Thakhampaeng P, Kaewsanit A, Mungthin M, et al. National trends in the prevalence of diabetic retinopathy among Thai patients with type 2 diabetes and its associated factors from 2014 to 2018. PLoS ONE. 2021;16(1):e0245801.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolde HF, Atsedeweyen A, Jember A, Awoke T, Mequanent M, Tsegaye AT, et al. Predictors of vascular complications among type 2 diabetes mellitus patients at University of Gondar Referral Hospital: a retrospective follow-up study. BMC Endocr Disorders. 2018;18(1):52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim YJ, Kim J-G, Lee JY, Lee KS, Joe SG, Park J-Y, et al. Development and Progression of Diabetic Retinopathy and Associated Risk Factors in Korean Patients with Type 2 Diabetes: The Experience of a Tertiary Center. J Korean Med Sci. 2014;29(12):1699\u0026ndash;705.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLima VC, Cavalieri GC, Lima MC, Nazario NO, Lima GC. Risk factors for diabetic retinopathy: a case\u0026ndash;control study. Int J Retina Vitreous. 2016;2(1):21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlemayehu HB, Tegegn MT, Tilahun MM. Prevalence and associated factors of visual impairment among adult diabetic patients visiting Adare General Hospital, Hawassa, South Ethiopia, 2022. PLoS ONE. 2022;17(10):e0276194.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeid MA, Ambelu A, Diress M, Yeshaw Y, Akalu Y, Dagnew B. Visual impairment and its predictors among people living with type 2 diabetes mellitus at Dessie town hospitals, Northeast Ethiopia: institution-based cross-sectional study. BMC Ophthalmol. 2022;22(1):52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan ZP, Ma JX. Risk factors for diabetic retinopathy in northern Chinese patients with type 2 diabetes mellitus. Int J Ophthalmol. 2016;9(8):1194\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-4231400/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4231400/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiabetic retinopathy is a microvascular complication of diabetes mellitus that is among the leading causes of irreversible blindness worldwide. There is a growing concern about diabetic retinopathy in Ethiopia associated with the increasing prevalence of diabetes. Limited studies have been conducted in Ethiopia that evaluated the magnitude of diabetic retinopathy and its associated factors among patients with type 2 diabetes. Thus, we aimed to assess the magnitude of diabetic retinopathy and its associated factors among patients with type 2 diabetes on follow-up at the diabetic clinic of Asella Referral and Teaching Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn institution-based cross-sectional study involving 428 patients with type 2 diabetes was conducted at the diabetic clinic of Asella Referral and Teaching Hospital. Participants were selected using a systematic random sampling technique. A descriptive analysis was performed to determine the prevalence of diabetic retinopathy. Bivariate binary logistic regression and multivariable logistic regression were conducted to determine factors associated with diabetic retinopathy, at α = 0.05 using adjusted odds ratio with 95% confidence interval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 428 participants, 53 (12.4%) had diabetic retinopathy (95% CI: 9.4, 15.9). Dyslipidemia (AOR = 2.538, 95% CI: 1.190, 5.412), poor medication adherence (AOR = 3.706, 95% CI: 1.730, 7.940), presence of other complications of diabetes (AOR = 2.537, 95% CI: 1.10, 5.850) were factors associated with diabetic retinopathy. Furthermore, those who exercise regularly (AOR = 0.048, 95% CI: 0.004, 0.565) had lower odds of having retinopathy than their counterpart.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prevalence of diabetic retinopathy in this study is lower than previous studies. Dyslipidemia, regular exercise, medication adherence, and the presence of other diabetic complications were factors significantly associated with diabetic retinopathy. Health education is recommended to promote exercise self-care and diabetes medication adherence. Early screening for other comorbidities and diabetes complications is also recommended.\u003c/p\u003e","manuscriptTitle":"Magnitude of diabetic retinopathy and its associated factors among patients with type 2 diabetes on follow up at diabetes clinic of Asella Referral and Teaching Hospital, Asella, Ethiopia: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 17:47:21","doi":"10.21203/rs.3.rs-4231400/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":"05b27eb3-2d85-4c6c-9c92-d68568c7724f","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-13T13:36:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-08 17:47:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4231400","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4231400","identity":"rs-4231400","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}