Incidence and predictors of diabetic ketoacidosis among adult type 1 and type 2 diabetes mellitus patients at public hospitals in Harari Region, eastern Ethiopia: A retrospective cohort study

Incidence and predictors of diabetic ketoacidosis among adult type 1 and type 2 diabetes mellitus patients at public hospitals in Harari Region, eastern Ethiopia: A retrospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Incidence and predictors of diabetic ketoacidosis among adult type 1 and type 2 diabetes mellitus patients at public hospitals in Harari Region, eastern Ethiopia: A retrospective cohort study Birhanu Shegene, Alemayehu Tesfaye, Mentesenot Seid Abate, Abdi Gari Negasa, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7278066/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Diabetic ketoacidosis (DKA) is the most prevalent and serious acute complication of diabetes mellitus. Over the past decade, the global incidence of DKA hospitalizations has risen, with recent studies reporting a 55% increase. Therefore, this study aimed to assess the incidence and identify predictors of DKA among adult patients with diabetes in eastern Ethiopia. Methods An institution-based retrospective cohort study was conducted in public hospitals in the Harari region of Ethiopia from January 1, 2019, to December 31, 2024, among 455 adults with diabetes mellitus. Data collection was performed using the Kobo toolbox, and analysis was carried out using STATA software version 17. The Cox proportional hazards regression model was applied to identify predictors of DKA. Adjusted hazard ratios (AHR) with 95% confidence intervals (CI) and corresponding p-values were computed. Results Out of the 446 patients included in the study, 110 (24.66%), 95%CI (20.87%-28.88%) developed diabetic ketoacidosis. The incidence rate of DKA was 1.1 cases per 100 person-months (95% CI: 0.9–1.3), with rates of 2.7 per 100 person-months for T1DM and 0.7 per 100 person-months for T2DM. Medication non-adherence (AHR: 2.27, 95% CI: 1.46, 3.54), poor glycemic control (AHR: 2.79, 95% CI: 1.72, 4.54), acute febrile illness (AHR: 2.15, 95% CI: 1.51, 3.07), urinary tract infection (AHR: 3.04, 95% CI: 1.99, 4.64) and overweight (AHR: 2.23, 95% CI: 1.45, 3.42) were predictors significantly associated with DKA. Conclusion The study revealed that diabetic ketoacidosis occurred in one out of four diabetic patients. Factors such as medication non-adherence, poor glycemic control, overweight, acute febrile illness, and urinary tract infections significantly increased the risk of DKA. Therefore, targeted follow-up care is essential for diabetic patients with these identified predictors to reduce the incidence of DKA. Incidence diabetic ketoacidosis predictors diabetes mellitus eastern Ethiopia Figures Figure 1 Figure 2 Introduction Diabetes mellitus (DM) is a chronic metabolic disorder characterized by persistent hyperglycemia. There are two types of diabetes mellitus: type 1 diabetes mellitus (Type 1 DM), which is caused by a lack of insulin due to β-cell destruction, and type 2 diabetes mellitus (Type 2 DM), which is related to impaired insulin action ( 1 ). Diabetic ketoacidosis (DKA), the most prevalent acute consequence of diabetes, is linked to increased mortality, disability, and a shortened life expectancy. It also results in significant health expenses for all societies, even in high-income countries ( 2 , 3 ). Type 1 diabetes's major acute metabolic consequence, DKA, is typified by metabolic acidosis, ketosis, and, in the majority of cases, hyperglycemia. Even though people with type 1diabetes are more acidotic, people with type 2 diabetes also need to be treated for acidosis because they can get DKA ( 4 ). Increased counter-regulatory hormones such as cortisol, glucagon, growth hormone, and catecholamines can result from a relative or absolute lack of insulin. These hormones promote glycogenolysis and gluconeogenesis, which leads to hyperglycemia. Chronic hyperglycemia can then lead to the accumulation of ketone bodies, which can lead to metabolic acidosis and diabetic ketoacidosis ( 5 , 6 ) This often recurs because of poor adherence to insulin therapy ( 7 ). Anorexia, nausea, vomiting, polyuria, thirst, stomach pain, and altered mental status are clinical signs of diabetic ketoacidosis (DKA). Intravenous fluid administration, identifying precipitating events, potassium replacement, and insulin administration are recommended therapies ( 8 , 9 ). Diabetic ketoacidosis is the leading cause of death in children and young adults, accounting for approximately 50% of deaths, with half of all deaths occurring in patients under the age of 24 ( 10 ). Treatment of this condition requires a significant amount of resources, with an estimated yearly total cost of $ 2.4 billion ( 6 , 11 ). In high-income nations ( 12 – 14 ), the mortality rate from DKA ranges from less than 1–4.5%, but in low- and middle-income countries, it ranges from 10–30% ( 15 – 19 ). According to reports, the mortality rate from DKA was approximately 23.6% in 2019 ( 20 ). Age and urban residency were found to be sociodemographic predictors of DKA in previous studies ( 21 – 23 ). Furthermore, Type 1 DM, comorbidity, infection before DKA onset, poor glycemic control, no family history of diabetes mellitus, infection, comorbidities, and stopping medication were all found to be predictors of DKA ( 7 , 16 , 24 , 25 ). Previous studies have revealed little information about the prevalence of DKA in Ethiopia ( 26 – 28 ). However, evidence regarding the incidence of DKA and its predictors in Ethiopia has been scarce, especially in the study area. For example, the overall incidence rate of diabetic ketoacidosis was determined to be 2.2 per 100 person-months in a single research carried out at Woldiya Comprehensive Specialized Hospital in northern Ethiopia ( 24 ). Therefore, this study aimed to assess the incidence of DKA and its predictors among adult patients with diabetes mellitus using a retrospective cohort study in eastern Ethiopia. Understanding the local incidence and specific predictors of DKA within the Ethiopian context is essential for developing targeted prevention strategies, optimizing resource allocation, and ultimately improving patient outcomes. Materials and methods Study setting, design, and period A retrospective cohort study was carried out involving adult diabetes mellitus (DM) patients undergoing long-term follow-up from January 1, 2019, to December 31, 2024. Data were collected from public hospitals in the Harari region, located in the Harari regional state approximately 526 kilometers from Addis Ababa, Ethiopia’s capital, during the period of January 1 to January 31, 2025. The Harari region has an estimated population of 283,000, with an equal male-to-female ratio( 29 ). The region is supported by a total of six hospitals, including two public, two private, one police, and one non-governmental hospital. This study took place in two public hospitals within the region: Hiwot Fana Comprehensive Specialized University Hospital (HFCSUH) and Jugal General Hospital (JGH). HFCSUH, a major teaching hospital with 210 beds, functions as a key referral center serving around 5.8 million people in eastern Ethiopia ( 30 ). In contrast, Jugal General Hospital primarily serves the people of Harar town and has 95 beds. Approximately 1,500 DM patients currently receive routine follow-up care at these two institutions, including 852 patients at HFCSUH and 648 at JGH ( 31 ). Populations The source population included all adult diabetic patients receiving long-term care at public hospitals in the Harari area. The study population comprised all adult diabetes patients on chronic follow-up at HFCSUH and JGH from January 1, 2019, to December 31, 2024. Eligibility criteria The study included all adult diabetic patients diagnosed between January 1, 2019, and December 31, 2024, receiving chronic follow-up treatment at public hospitals of Harari region. However, individuals who were brought in from other facilities or who acquired DKA at the time of their original diabetes diagnosis were not included. Sample size and sampling procedures The final sample size of 455 was calculated using the Schoenfeld formula in Stata version 17, based on a power analysis for the Cox proportional hazards model.( 32 ). This was estimated by considering a 31% event probability and an adjusted hazard ratio (AHR) of 0.59 for urban residence, as reported in a previous study ( 24 ). We assumed a 95% confidence interval, 80% power, and 20% anticipated withdrawal rate. All public hospitals in the Harari region —namely, HFCSUH and JGH —were included in the study. Prior to allocating the sample, the number of newly diagnosed diabetes mellitus (DM) patients attending follow-up care at both hospitals during the study period was identified. A total of 1,896 adult DM patients were recorded—998 from HFCSUH and 898 from JGH—between January 1, 2019, and December 31, 2024. The overall sample size of 455 was distributed proportionally between the two hospitals according to the number of diabetic patients receiving follow-up care at each site. A compilation of medical record numbers for adults with diabetes was then created from the diabetic follow-up logbooks of both hospitals. Finally, the required participants were randomly selected using Excel-generated random numbers from this sampling frame. Study variables The incidence of DKA was the dependent variables, while the independent variables encompassed socio-demographic characteristics ( age, sex, residence, community health insurance), clinical factors ( BMI, family history of diabetes, recent infection, type of diabetes, glycemic control, duration of diabetes, presence of comorbidities, presence of chronic diabetic complications), and treatment factors (frequency of follow-up, type of medication used, treatment duration, and medication non-adherence). Data collection instrument and procedure A data extraction format that was modified from pertinent literature was used to gather the data ( 26 , 28 , 33 – 37 ). The format consisted of five parts: general information, socio-demographic factors, clinical factors, treatment-related factors, and the follow-up form. Initially, all DM patients on long-term follow-up from January 1, 2019, to December 31, 2024, were identified using the diabetes log book. From this group, eligible adult patients were then randomly chosen through Excel-generated random numbers, excluding those who developed diabetic ketoacidosis (DKA) at initial diagnosis and transferred-in cases. Data were extracted from patient cards using a pretested data collection tool. Medical registration numbers were utilized to locate the records reviewed. The data collection was carried out by two BSc public health professionals experienced in data handling, under the supervision of a trained BSc nurse specialized in chronic follow-up and a public health expert with a master's degree. Data quality control To ensure data quality, data collectors and supervisors received a one-day training session covering the data extraction process, data collection tools, and the objectives of the study. Prior to the main data collection, a pretest was conducted on 5% (23 participants) of the total sample at HFCSUH. These pretest samples were excluded from the final analysis. Based on the pretest findings, necessary adjustments were made to the data extraction format. One week before data collection began, patient charts were reviewed to confirm the adequacy of the tools, ensure timely completion of the checklists, and verify the completeness of chart data. Continuous supervision and close monitoring were provided by both the principal investigator and the supervisor throughout the data collection period. Daily feedback was given to data collectors to address any issues promptly. All collected data were checked for completeness before analysis. To reduce misclassification bias, consistent criteria were applied for clinical variables with multiple diagnostic definitions. Missing data were managed through a complete case analysis approach. Operational definitions Event: The occurrence of DKA Censored: Adults with diabetes who did not experience DKA during the follow-up period (transferred out, died, lost to follow-up, or had not developed DKA by the end of the study). Incomplete records: These are charts that do not contain all necessary information for the variables of the date of DM diagnosis, the date DKA occurred, and the follow-up history after diagnosis. DKA status: 'Yes' for DKA indicates the occurrence of the first incident of DKA, based on the clinical decisions of the physicians and obtained from the patient’s medical records. This status is also validated based on the results of biochemical parameters: serum glucose > 250 mg/dL, acidosis (arterial blood pH < 7.3 and bicarbonate < 15 mEq/L), and urine ketones ≥ 2+ (38).'No' for DKA indicates that the patient did not experience any incidents of DKA during the follow-up period and is considered censored. Medication non-adherence: Records showing that the patient has ceased (discontinued) taking prescribed anti-diabetic medications (39). Glycemic control: Good glycemic control is defined as an average fasting blood sugar (FBS) level between 70 to 130 mg/dl or an HbA1c of less than 7%. Poor glycemic control is indicated by an average FBS greater than 130 mg/dl or less than 70 mg/dl, or an HbA1c greater than 7% (40). Duration of treatment started: Patients who started treatment on the same day of DM diagnosis by the doctor are considered immediate users; otherwise, they are considered non-immediate users (41). Adult : Patients who are 18 years or older. Data processing and analysis Following data collection, the data were exported from Kobo Toolbox to Microsoft Excel and subsequently imported into STATA version 17 for further cleaning and analysis. Basic descriptive statistics were performed. Each patient was monitored until the development of diabetic ketoacidosis (DKA) or until censoring, whichever happened first. Survival analysis was utilized as the statistical method to assess the relationship between DKA incidence and its predictors. The probability of remaining free from DKA was calculated in months, based on the time from DM diagnosis to either DKA occurrence or censoring. The incidence rate of DKA was also determined. Life tables were employed to estimate survival probabilities at various time points after DM diagnosis. Additionally, Kaplan-Meier survival curves and log-rank tests were conducted to estimate survival probabilities and to compare survival across different predictor groups statistically. The Schoenfeld residuals test for the proportional hazards assumption was performed for each individual predictor as well as for global tests (42). All predictors had a P-value greater than 0.05, and the overall global test showed a P-value of 0.134, suggesting that the proportional hazards assumption was not violated. Plots were utilized to assess the predictors in the model, and the variables in the final model displayed parallel curves, indicating proportional hazards among the groups. The presence of multicollinearity was evaluated using the variance inflation factor (VIF), which was 1.81, indicating no significant multicollinearity issues. A Cox proportional hazards regression model was used to identify predictors. Variables with a p-value less than 0.25 in the bi-variable analysis were considered candidates for the multivariable Cox proportional hazards model. Hazard ratios (HR) along with their respective 95% confidence intervals (CI) were reported to indicate the significance and strength of the relationship with the dependent variable. Variables with a p-value below 0.05 in the multivariable model were considered significant and independently associated with the outcome. The overall model fit was assessed using the Cox-Snell residual plot. The close correspondence between the Cox-Snell residual line and the 45-degree cumulative hazard line suggested a good model fit, as the residual line closely tracked the bisector with minimal deviations ( Figure 1 ). Ethical Considerations Ethical clearance and approval were granted by the Institutional Health Research Ethics Review Committee (IHRERC) of Haramaya University College of Health and Medical Sciences. A cooperation letter was secured from Haramaya University College of Health and Medical Sciences addressed to the respective hospitals. Informed, voluntary, written, and signed consent was obtained from the heads of the hospitals. Following ethical approval, data were collected from patient charts using a checklist, with codes applied to ensure confidentiality. The completed checklists were stored securely in a locked location. The entered data were password-protected on the computer. Access to the data was restricted to the principal investigator. Results Socio-demographic factors Of the 455 patient cards reviewed, 446 were part of the final analysis, whereas 9 patients (2%) were excluded due to incomplete information and lack of follow-up history. Among the total data, 259 patients (58.07%) were from HFCSUH and 187 (41.93%) from Jugal General Hospital. Of the 446 adults with diabetes, 49.78% were female, and 206 patients (46.19%) lived in rural areas. The median age at diabetes diagnosis was 46 years, with an interquartile range (IQR) of 27 years. Additionally, 158 patients (35.43%) did not have community-based health insurance. ( Table 1 ). Clinical and treatment characteristics of diabetes mellitus patients According to the study, three-fourths (75.78%) of the diabetes patients, had T2DM. The majority, 391 patients (90.51%), reported no family history of diabetes. Among the participants, 157 (35.20%) had an infection, and 201 (45.07%) had uncontrolled blood sugar levels. At baseline, 253 patients (56.73%) had at least one comorbidity, and 72 patients (17.38%) had more than one. Cardiovascular disorders were the most prevalent comorbidities, affecting 156 patients (34.98%), of which hypertension accounts for 135 cases (86.53%). Regarding chronic complications of diabetes, 12 adults (2.69%) experienced diabetic neuropathy, while 11 adults (2.47%) had diabetic nephropathy. Most patients, 284 (63.68%), had a normal body mass index (BMI), and 157 (35.20%) had irregular follow-up appointments. Additionally, 306 patients (68.61%) were prescribed oral hypoglycemic agents at diagnosis, and 93 (20.85%) of the adult diabetes patients were noncompliant with their medication ( Table 2 ). Incidence of diabetic ketoacidosis Among 446 diabetes patients monitored over five years, 110 (24.66%) developed diabetic ketoacidosis, with a 95% CI of 20.87% to 28.88%. The overall incidence rate of DKA during a total of 10,037 person-months of observation was 1.1 cases per 100 person-months (95% CI: 0.9-1.3), with rates of 2.7 per 100 person-months for type 1 diabetes mellitus and 0.7 per 100 person-months for type 2 diabetes mellitus. The incidence rate was particularly high among younger individuals (aged 18–44) and rural residents, both showing a rate of 1.7 per 100 person-months of observation (95% CI: 1.3–2.1). Throughout the follow-up period, the highest incidence of DKA occurred within the first 0 to 6 months, while the lowest incidence was noted during the 36 to 42 months follow-up interval. ( Table 3 ). The Kaplan–Meier estimate indicated a high probability of DKA-free survival among adult diabetes patients at 1.2 months of observation, recorded at 99.55%. However, this probability gradually declined with longer follow-up periods, dropping to a minimum of 58.35% by 59.87 months of observation. The median DKA-free survival time was undefined, as more than 50% of the patients remained free from DKA throughout the follow-up period ( Figure 2 ). Predictors of DKA In the bi-variable Cox regression analysis, twelve variables were identified as being associated with DKA at a significance level of p ≤ 0.25. These factors included community-based health insurance (CBHI), residence, age, medication non-compliance, glycemic control, body mass index (BMI), type of diabetes mellitus, trauma, drugs, acute febrile illness (AFI), respiratory infections, and urinary tract infections (UTIs). In the multivariable Cox regression analysis, five variables were identified as predictors of DKA. These were Medication non-compliance, Glycemic control, BMI, AFI, and UTI. Accordingly, DM patients with medication non-adherence had a 2-fold higher probability of DKA than patients with medication adherence (AHR: 2.27, 95% CI: 1.46, 3.54). The risk of DKA among DM patients was 2.8 times higher among patients without glycemic control as compared to those with glycemic control (AHR: 2.79, 95% CI: 1.72, 4.54). Regarding BMI, DM patients who were overweight had 2.23 times (AHR: 2.23, 95% CI: 1.45, 3.42) higher hazard of developing DKA compared to those with normal BMI. Patients who had experienced AFI were 2.15 times more likely to develop DKA compared to those who did not experience AFI (AHR: 2.15, 95% CI: 1.51, 3.07). The hazard of DKA among DM patients was 3 times higher among patients with UTI as compared to those without UTI (AHR: 3.04, 95% CI: 1.99, 4.64) ( Table 4 ). Discussion In this study, 24.66% (95% CI: 20.87–28.88%) of patients with diabetes mellitus (DM) experienced diabetic ketoacidosis (DKA). The overall incidence rate of DKA was 1.1 cases per 100 person-months (95% CI: 0.9–1.3), equivalent to 13.3 cases per 100 person-years (95% CI: 11.1–16.1). Among these, the incidence rates were 2.7 and 0.7 per 100 person-months for patients with type 1 diabetes (T1DM) and type 2 diabetes (T2DM), respectively. Factors such as medication non-compliance, glycemic control, body mass index (BMI), acute febrile illness (AFI), and urinary tract infection (UTI) were significantly linked to an increased risk of developing DKA. The incidence rate of DKA in this study was lower than the study in Woldiya, Ethiopia (2.2 per 100 PM ( 43 ). This could be due to differences in types of DM and residence. In our study, 75.78% of individuals had Type 2 diabetes, while in Woldiya, 76.15% were diagnosed with Type 1 diabetes. Additionally, 53.81% of the participants in our study lived in urban areas, whereas 55.4% of those in the Woldiya research were rural residents. The incidence rate of DKA observed in this study was greater than that reported in a study conducted in Western Australia ( 44 ),which had a rate of 0.04 per 100 PYs in China ( 45 ) with a rate of 1.21 per 100 person-years, in Spain ( 46 )Which reported 0.06 per 100 PYs, and a study performed in the USA, which indicates a rate of 0.17 per 100 PYs. One potential reason for this variation could be the differences in sample size and duration of follow-up; for example, a study conducted in China tracked participants over a twelve-year period. Moreover, the discrepancy may stem from differences in study design, with the Western Australia research using a prospective cohort approach involving 1,724 participants, while the current study applied a retrospective cohort design. Additionally, variations in socio-economic and socio-cultural factors affecting health-seeking behaviors might also contribute to this difference. Medication non-adherence was positively associated with DKA in this study. This finding aligns with a previous study conducted in the Amhara region, Ethiopia ( 47 )and Cameroon ( 48 ), indicating a persistent relationship between medication non-compliance and increased risk of DKA. The increased risk is probably attributed to the direct effect of non-compliance on blood glucose control. When patients fail to adhere to their medication regimen, their blood glucose levels can rise substantially, leading to a greater chance of developing diabetic ketoacidosis (DKA), a severe complication marked by elevated blood sugar and ketone levels ( 49 ). Patients with poor glycemic control were positively associated with DKA. These results are consistent with research done in Woldiya, Ethiopia ( 43 ), studies done at Debre Markos Referral Hospital ( 50 ), and Ayder Referral Hospital ( 51 ). This might be because poor glycemic control is one of the markers of DKA ( 52 ). Poor glycemic control leads to consistently high blood glucose levels. When blood glucose levels rise significantly, it can result in osmotic diuresis, causing dehydration and increasing the risk of DKA. Elevated glucose levels also promote the breakdown of fat for energy, leading to the production of ketones, which can cause ketoacidosis ( 53 ). In this study, DM patients who were overweight had a higher likelihood of developing diabetic ketoacidosis (DKA). This finding is supported by research from South West Ethiopia ( 54 ) and Western Australia ( 44 ). Being overweight may contribute to insulin resistance and increased levels of fatty acids. Elevated free fatty acids can promote insulin resistance and stimulate ketogenesis, which plays a critical role in the development of DKA ( 55 ). Although DKA typically presents in lean individuals with Type 1 diabetes, there is an epidemiological trend showing a rise in DKA cases among people with Type 2 diabetes ( 56 ). Having AFI was significantly associated with DKA among DM patients. These results are consistent with research done in Woldiya, Ethiopia ( 43 ), in South Africa( 57 ) and in Dilla University Referral Hospital ( 58 ). This might be due to increased insulin resistance, the release of counter-regulatory hormones, and dehydration in Acute febrile illnesses ( 59 ). DM Patients with UTI were more prone to develop DKA than those without the condition. This aligns with findings from studies conducted in Jimma, Ethiopia ( 60 ); Bahir Dar, Ethiopia ( 61 ); Cameroon ( 48 ), and Iraq ( 62 ), all of which identified acute recent illness as a significant predictor of DKA. The likely reason for this association is that UTI can lead to physiological stress, which may increase insulin resistance, and it often causes symptoms such as fever and increased urination, which can lead to dehydration ( 63 ). This dehydration can ultimately contribute to the onset of DKA. These findings highlight the need for further research to enhance understanding of the predictors of diabetic ketoacidosis and to assess its implications for health policy. Strengths and limitations of this study This study analyzed five years of follow-up data, offering a more thorough insight into the incidence of DKA over time. However, the retrospective study design restricted the ability to include all potential factors influencing patients' DKA status. The diagnosis of DKA was based on physicians’ clinical judgment, which may not always be fully reliable, raising the possibility of misclassification and documentation bias that could impact the results' validity. Furthermore, the study lacked standardized laboratory confirmation for DKA, may have underreported comorbidities or infections, was unable to account for time-varying exposures, and its findings may have limited applicability outside of public hospital patient populations. Conclusion In this study, diabetic ketoacidosis (DKA) was observed in one in every four patients with diabetes mellitus receiving care at public hospitals. A history of medication non-adherence, poor glycemic control, being overweight, acute febrile illness (AFI), and urinary tract infection (UTI) were significantly linked to an increased risk of developing DKA. We recommend placing particular focus on follow-up care for diabetic patients presenting these risk factors to help lower the incidence of DKA. Lastly, we encourage conducting further prospective follow-up research that incorporates time-dependent covariates for variables that may change over time and addresses any missing factors. Declarations Ethical approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki. Approval for studies involving humans was granted by the Institutional Health Research Ethics Review Committee of Haramaya University College of Health and Medical Sciences (Ref. No. IHRERC/149/2024). The research adhered to local legislation and institutional requirements. The ethics committee waived the requirement for written informed consent from participants or their legal guardians/next of kin due to the retrospective nature of the study. Consent for publication Not applicable Data availability The data used to support the findings of this study are available upon request from the corresponding author. Clinical trial number Not applicable. Funding The author(s) received no financial assistance for the research, authorship, or publication of this article. Competing interests The author(s) have declared no potential conflicts of interest in the research, authorship, and/or publication of this article. Authors’ contributions All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or all these areas; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work. Acknowledgment s We extend our gratitude to the administrative staff and card room workers at the Harari region public hospital for their collaboration. Additionally, we appreciate the dedication of the data collectors and supervisors throughout the data collection process. References Baynes HJJdm. Classification, pathophysiology, diagnosis and management of diabetes mellitus. 2015;6(5):1-9. Benoit SRJMM, report mw. Trends in diabetic ketoacidosis hospitalizations and in-hospital mortality—United States, 2000–2014. 2018;67. 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Acute Complications of Diabetes and its Predictors among Adult Diabetic Patients at Jimma Medical Center, Southwest Ethiopia. Diabetes, metabolic syndrome and obesity : targets and therapy. 2020;13:1237-42. CSA. Population Size by Sex, Region, Zone and Wereda 2023 [Available from: https://www.statsethiopia.gov.et/. Cheru A, Edessa D, Regassa LD, Gobena T. Incidence and predictors of chronic kidney disease among patients with diabetes treated at governmental hospitals of Harari Region, eastern Ethiopia, 2022. Front Public Health. 2023;11:1290554. HMIS. Hiwot Fana Comprehensive Specialized University Hospital and Jugal General Hospital HMIS report. 2024. Schoenfeld DJB. Partial residuals for the proportional hazards regression model. 1982;69(1):239-41. Abejew AA, Belay AZ, Kerie MWJAiPH. Diabetic complications among adult diabetic patients of a tertiary hospital in northeast Ethiopia. 2015;2015. Desse TA, Eshetie TC, Gudina EK. Predictors and treatment outcome of hyperglycemic emergencies at Jimma University Specialized Hospital, southwest Ethiopia. BMC research notes. 2015;8:553. Gebre BB, Assefa ZMJBrn. Magnitude and associated factors of diabetic complication among diabetic patients attending Gurage zone hospitals, South West Ethiopia. 2019;12:1-6. Tittel S, Sondern K, Weyer M, Poeplau T, Sauer B, Schebek M, et al. Multicentre analysis of hyperglycaemic hyperosmolar state and diabetic ketoacidosis in type 1 and type 2 diabetes. 2020;57:1245-53. Abate MD, Semachew A, Emishaw S, Meseret F, Azmeraw M, Algaw D, et al. Incidence and predictors of hyperglycemic emergencies among adult diabetic patients in Bahir Dar city public hospitals, Northwest Ethiopia, 2021: A multicenter retrospective follow-up study. Front Public Health. 2023;11:1116713. Hassan EM, Mushtaq H, Mahmoud EE, Chhibber S, Saleem S, Issa A, et al. Overlap of diabetic ketoacidosis and hyperosmolar hyperglycemic state. World journal of clinical cases. 2022;10(32):11702-11. Araya EM, Gebrezgabiher HA, Tekulu GH, Alema NM, Getnet D, Gebru HT, et al. Medication Non-Adherence and Associated Factors Among Diabetic Patients Visiting General Hospitals in the Eastern Zone of Tigrai, Northern Ethiopia. Patient preference and adherence. 2020;14:2071-83. Association AD. Standards of Medical Care in Diabetes—2020 Abridged for Primary Care Providers. Clinical Diabetes. 2020;38(1):10-38. Mansour AA, Abdu-Alla MA-A-KJBJoM, Research M. Predictors of diabetic ketoacidosis among patients with type 1 diabetes mellitus seen in the emergency unit. 2016;11(10):1. Schoenfeld D. Partial Residuals for The Proportional Hazards Regression Model. Biometrika. 1982;69(1):239-41. Zewdu B, Belachew T, Ahmed K, Tilahun L, Dagnaw K. Incidence and determinants of diabetic ketoacidosis among people with diabetes in Woldiya comprehensive specialized hospital, Ethiopia: a retrospective cohort study. BMC Endocrine Disorders. 2024;24(1):34. Davis TM, Davis W. Incidence and associates of diabetic ketoacidosis in a community-based cohort: the Fremantle Diabetes Study Phase II. BMJ Open Diabetes Research and Care. 2020;8(1):e000983. Ou H-T, Lee T-Y, Li C-Y, Wu J-S, Sun Z-J. Incidence of diabetes-related complications in Chinese patients with type 1 diabetes: a population-based longitudinal cohort study in Taiwan. BMJ open. 2017;7(6):e015117. Guisado-Vasco P, Cano-Megías M, Carrasco-de la Fuente M, Corres-González J, Matei AM, González-Albarrán O. Clinical features, mortality, hospital admission, and length of stay of a cohort of adult patients with diabetic ketoacidosis attending the emergency room of a tertiary hospital in Spain. Endocrinología y Nutrición (English Edition). 2015;62(6):277-84. Getie A, Wondmieneh A, Bimerew M, Gedefaw G, Demis A. Determinants of diabetes ketoacidosis among diabetes mellitus patients at North Wollo and Waghimra zone public hospitals, Amhara region, Northern Ethiopia. BMC endocrine disorders. 2021;21:1-9. Nkoke C, Bain LE, Makoge C, Teuwafeu D, Mapina A, Nkouonlack C, et al. Profile and outcomes of patients admitted with hyperglycemic emergencies in the Buea Regional Hospital in Cameroon. Pan African medical journal. 2021;39(1). Araya EM, Gebrezgabiher HA, Tekulu GH, Alema NM, Getnet D, Gebru HT, et al. Medication non-adherence and associated factors among diabetic patients visiting general hospitals in the eastern zone of Tigrai, Northern Ethiopia. Patient preference and adherence. 2020:2071-83. Kidanie BB, Alem G, Zeleke H, Gedfew M, Edemealem A, Andualem A. Determinants of diabetic complication among adult diabetic patients in Debre Markos referral hospital, northwest Ethiopia, 2018: unmatched case control study. Diabetes, Metabolic Syndrome and Obesity. 2020:237-45. Hintsa S, Dube L, Abay M, Angesom T, Workicho A. Determinants of diabetic nephropathy in Ayder Referral Hospital, Northern Ethiopia: a case-control study. PloS one. 2017;12(4):e0173566. Duca LM, Wang B, Rewers M, Rewers A. Diabetic ketoacidosis at diagnosis of type 1 diabetes predicts poor long-term glycemic control. Diabetes care. 2017;40(9):1249-55. Yahaya JJ, Doya IF, Morgan ED, Ngaiza AI, Bintabara D. Poor glycemic control and associated factors among patients with type 2 diabetes mellitus: A cross-sectional study. Scientific Reports. 2023;13(1):9673. Gebre BB, Assefa ZM. Magnitude and associated factors of diabetic complication among diabetic patients attending Gurage zone hospitals, South West Ethiopia. BMC research notes. 2019;12:1-6. Klein S, Gastaldelli A, Yki-Järvinen H, Scherer PEJCm. Why does obesity cause diabetes? 2022;34(1):11-20. Wang Y, Desai M, Ryan PB, DeFalco FJ, Schuemie MJ, Stang PE, et al. Incidence of diabetic ketoacidosis among patients with type 2 diabetes mellitus treated with SGLT2 inhibitors and other antihyperglycemic agents. Diabetes research and clinical practice. 2017;128:83-90. Ndebele NF, Naidoo M. The management of diabetic ketoacidosis at a rural regional hospital in KwaZulu-Natal. African Journal of Primary Health Care and Family Medicine. 2018;10(1):1-6. Eskeziya A, Girma Z, Mandefreo B, Haftu A. Prevalence of Diabetic Keto Acidosis and Associated Factors among Newly Diagnosed Patients with Type One Diabetic Mellitus at Dilla University Referral Hospital, September 9th/2017–May 30th/2019: South Ethiopia; Crossectional Study. J Healthcare. 2020;3(1):33-8. Blanchard F, Charbit J, Van der Meersch G, Popoff B, Picod A, Cohen R, et al. Early sepsis markers in patients admitted to intensive care unit with moderate-to-severe diabetic ketoacidosis. Annals of intensive care. 2020;10:1-10. Desse TA, Eshetie TC, Gudina EK. Predictors and treatment outcome of hyperglycemic emergencies at Jimma University Specialized Hospital, southwest Ethiopia. BMC research notes. 2015;8:1-8. Abate MD, Semachew A, Emishaw S, Meseret F, Azmeraw M, Algaw D, et al. Incidence and predictors of hyperglycemic emergencies among adult diabetic patients in Bahir Dar city public hospitals, Northwest Ethiopia, 2021: A multicenter retrospective follow-up study. Frontiers in Public Health. 2023;11:1116713. Mansour A, Abdu-Alla M. Predictors of diabetic ketoacidosis among patients with type 1 diabetes mellitus seen in the emergency unit. British Journal of Medicine and Medical Research. 2016;11(10):1-12. Nitzan O, Elias M, Chazan B, Saliba W. Urinary tract infections in patients with type 2 diabetes mellitus: review of prevalence, diagnosis, and management. Diabetes, metabolic syndrome and obesity: targets and therapy. 2015:129-36. Tables Table 1: Incidence of DKA and socio-demographic characteristics of diabetes patients at public hospitals of Harari region, Eastern Ethiopia, 2025. (N=446) Variables Category DKA Status Total (%) Developed DKA(n=110) Censored (n=336) Health facility HFCSUH 60 199 259(58.07) JGH 50 137 187(41.93) Age at diagnosis 18-44 74 121 195(43.72) 45-64 35 158 193(43.27) ≥ 65 1 57 58(13.00) Sex Male 57 167 224(50.22) Female 53 169 222(49.78) Residence Urban 39 201 240(53.81) Rural 71 135 206(46.19) CBHI Yes 46 242 288(64.57) No 64 94 158(35.43) CBHI, community-based health insurance; HFCSUH, Hiwot Fana Comprehensive Specialized University Hospital; JGH, Jugal General Hospital Table 2: Clinical and treatment-related results of adult patients with diabetes at public hospitals of Harari region, Eastern Ethiopia, 2025. (N=446) Variables Category Frequency (%) Type of DM Type2DM 338(75.78) Type1DM 108(24.22) DM duration <3 years 347(77.80) ≥ 3 years 99(22.20) Family history of DM No 391(90.51) Yes 41(9.49) Glycemic control Good glycemic control 245(54.93) Poor glycemic control 201(45.07) Trauma No 430(96.41) Yes 16(3.59) Body mass index status Normal 284(63.68) Overweight 124(27.80) Obesity 24(5.38) Underweight 14(3.14) Acute infection Urinary tract infection 63(14.13) Respiratory infection 46(10.31) Gastrointestinal infection 25(5.61) Acute febrile illness 14(3.14) Myocardial infarction 10(2.24) Others* 17(3.81) Comorbidities Cardiovascular disorder 156(34.98) Renal disease 27(6.05) Chronic respiratory disorder 14(3.14) Liver disease 8(1.79) HIV/AIDS 8(1.79) Benign prostate hyperplasia 10(2.24) Psychiatric disorder 5(1.12) Others** 18(4.03) More than one comorbidity 47(10.54) Chronic DM complication Diabetic neuropathy 12(2.69) Diabetic nephropathy 11(2.47) Diabetic retinopathy 8(1.79) Diabetic foot ulcers 4(0.89) Type of drug Oral hypoglycemic agent 306(68.61) Insulin 131(29.37) Oral and Insulin 9(2.02) Medication non-adherence No 353(79.15) Yes 93(20.85) Follow up frequency Regular 289(64.80) Irregular 157(35.20) Duration of treatment Immediate 448(94.92) Not immediate 24(5.08) *Others include osteoarthritis, acute otitis media, sexually transmitted infections, necrotizing fasciitis, periodontitis, pyomyositis, cellulitis, and carbuncle. **Others include rectal cancer, dyslipidemia, hypothyroidism, epilepsy, Parkinson's disease, hyperthyroidism, goiter, and cholelithiasis Table 3: Life table showing the survival to develop DKA among adult diabetic patients at public hospitals of Harari region, Eastern Ethiopia, 2025. (N=446) Interval in months Patients at risk N o of DKA cases Censored Cumulative Survival SD. Error [95% CI] L U (0 6] 446 33 31 0.0767 0.0128 0.0551 0.1061 (6 12] 382 22 61 0.1344 0.0169 0.1048 0.1716 (12 18] 299 23 70 0.2099 0.0216 0.1711 0.2559 (18 24] 206 6 27 0.2345 0.0231 0.1928 0.2835 (24 30] 173 11 41 0.2897 0.0268 0.2409 0.3459 (30 36] 121 6 15 0.3272 0.0294 0.2733 0.3886 (36 42] 100 2 11 0.3415 0.0305 0.2855 0.4049 (42 48] 87 4 26 0.3771 0.0336 0.3151 0.4467 (48 54] 57 3 18 0.4160 0.0383 0.3453 0.4950 (54 60] 36 0 36 0.4160 0.0383 0.3453 0.4950 Table 4. Bivariate and multivariate Cox regression analysis of predictors of DKA among DM patients at public hospitals in the Harari region, East Ethiopia, 2025. Variable Category DKA CHR (95% CI) AHR (95% CI) P-value Censored (n=336) Event ( n=110) CBHI Yes 242 46 1 1 No 94 64 3.09 (2.12- 4.52) 1.45 (.95- 2.21) 0.086 Residence Rural 135 71 2.54(1.72-3.75) 1.39 (.90-2.14) 0.142 Urban 201 39 1 1 Age 0.95(0.94-0.97) 0.99(0.97-1.01) 0.548 Medication non-adherence No 299 54 1 1 Yes 37 56 5.23(3.59-7.63) 2.27 (1.46-3.54) 0.001 glycemic control Controlled 218 27 1 1 Uncontrolled 118 83 5.01(3.24-7.76) 2.79 (1.72-4.54) 0.001 BMI Normal 233 51 1 1 Underweight 8 6 2.71 (1.16- 6.33) 1.10 (0.45-2.72) 0.823 Overweight 76 48 2.53 (1.71- 3.76) 2.23(1.45-3.42) 0.001 Obesity 19 5 1.37(0.55- 3.44) 2.44(0.90-6.63) 0.081 Types of DM Type 2 DM 286 52 1 1 Type 1 DM 50 58 3.97(2.73- 5.77) 1.55 (.65-3.68) 0.320 Trauma No 327 103 1 1 Yes 9 7 2.92(1.35- 6.29) 2.14(.90-5.07) 0.084 Drugs Oral 261 45 1 1 Insulin 69 62 3.54 (2.41-5.20) 1.45 (.68-3.14) 0.334 Oral and Insulin 6 3 1.97(.61-6.35) 1.40(.41- 4.75) 0.588 AFI Yes 6 8 2.94(1.43-6.05) 2.89 (1.33- 6.28) 0.007 No 330 102 1 1 Respiratory infection Yes 26 20 2.23(1.37-3.63) 1.52 (.89-2.62) 0.127 No 310 90 1 1 UTI No 303 80 1 1 Yes 33 30 3.04(1.99-4.64) 2.53(1.59- 4.06) 0.001 CBHI, community-based health insurance; BMI, body mass index; AFI, Acute febrile illness; UTI, Urinary tract infection Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7278066","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512647461,"identity":"9476f7b3-232a-4800-aba6-6b33ea45df2c","order_by":0,"name":"Birhanu Shegene","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACA2YwdQDC+wDEbOykaGGcAdLCTEgLA5IWZh4wSUCLOTvvwwcf/tyRk3fvPfjY5tc2eT5mBsYPH3Nwa7FsZjc2nNn2zNjwzLlk49y+24ZtzAzMkjO34XHYYTY2ad6Gw4kbZ+SYSef23GYEamFj5sWvhf33nz+H6zfOf2P+27Lntj0xWoBBxHY4QV6Cx4yZ4cftRIJaLJvZmCV72w4bbuDJMZbsbbid3MbM2IzXL+b8xxg//PhzWF6+/YwhkHHbdn5788EPH/FoQbjwAJBgbAMxGRuIUA8E8mB1f4hTPApGwSgYBSMLAAD58lBXDCVtwgAAAABJRU5ErkJggg==","orcid":"","institution":"Haramaya University","correspondingAuthor":true,"prefix":"","firstName":"Birhanu","middleName":"","lastName":"Shegene","suffix":""},{"id":512647462,"identity":"82f7d687-8a37-4747-8bc3-3de3ca63b67f","order_by":1,"name":"Alemayehu Tesfaye","email":"","orcid":"","institution":"Haramaya University","correspondingAuthor":false,"prefix":"","firstName":"Alemayehu","middleName":"","lastName":"Tesfaye","suffix":""},{"id":512647463,"identity":"ed99816f-96c8-4d68-8d52-384fe122be33","order_by":2,"name":"Mentesenot Seid Abate","email":"","orcid":"","institution":"Haramaya University","correspondingAuthor":false,"prefix":"","firstName":"Mentesenot","middleName":"Seid","lastName":"Abate","suffix":""},{"id":512647464,"identity":"a4d03ebd-f678-4dd4-af6d-5caff18bb99d","order_by":3,"name":"Abdi Gari Negasa","email":"","orcid":"","institution":"Haramaya University","correspondingAuthor":false,"prefix":"","firstName":"Abdi","middleName":"Gari","lastName":"Negasa","suffix":""},{"id":512647465,"identity":"2fdaf3d8-ce86-4b8c-bc05-a893cb7be4ce","order_by":4,"name":"Obsan Kassa","email":"","orcid":"","institution":"Haramaya University","correspondingAuthor":false,"prefix":"","firstName":"Obsan","middleName":"","lastName":"Kassa","suffix":""},{"id":512647466,"identity":"a206f455-3912-4dd2-a4e7-b745c3a252db","order_by":5,"name":"Dawit Firdisa","email":"","orcid":"","institution":"Haramaya University","correspondingAuthor":false,"prefix":"","firstName":"Dawit","middleName":"","lastName":"Firdisa","suffix":""}],"badges":[],"createdAt":"2025-08-02 11:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7278066/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7278066/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91077047,"identity":"c3d3f77f-ef99-43e2-92eb-abe3cd736bf1","added_by":"auto","created_at":"2025-09-11 11:13:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":198583,"visible":true,"origin":"","legend":"\u003cp\u003eCox–Snell residual plots for the Cox regression model of adult patients with diabetes at public hospitals of Harari region, Eastern Ethiopia, 2025. (N=446)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7278066/v1/27ae82041fe1f60c92d2defd.png"},{"id":91075157,"identity":"9bbf8d1f-a98e-435c-b59c-250ad9f8a2a7","added_by":"auto","created_at":"2025-09-11 11:05:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":228231,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curve of DKA-free survival estimate of adult patients with diabetes at public hospitals of Harari region, Eastern Ethiopia, 2025. (N=446)\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7278066/v1/d8af2625d04c4a989c20f81a.png"},{"id":91080086,"identity":"fbb35318-27fb-4e13-a8e1-a1867da77198","added_by":"auto","created_at":"2025-09-11 11:29:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1558903,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7278066/v1/e631acab-72b6-4d44-9f48-2149ca6613da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Incidence and predictors of diabetic ketoacidosis among adult type 1 and type 2 diabetes mellitus patients at public hospitals in Harari Region, eastern Ethiopia: A retrospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetes mellitus (DM) is a chronic metabolic disorder characterized by persistent hyperglycemia. There are two types of diabetes mellitus: type 1 diabetes mellitus (Type 1 DM), which is caused by a lack of insulin due to β-cell destruction, and type 2 diabetes mellitus (Type 2 DM), which is related to impaired insulin action (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Diabetic ketoacidosis (DKA), the most prevalent acute consequence of diabetes, is linked to increased mortality, disability, and a shortened life expectancy. It also results in significant health expenses for all societies, even in high-income countries (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Type 1 diabetes's major acute metabolic consequence, DKA, is typified by metabolic acidosis, ketosis, and, in the majority of cases, hyperglycemia. Even though people with type 1diabetes are more acidotic, people with type 2 diabetes also need to be treated for acidosis because they can get DKA (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIncreased counter-regulatory hormones such as cortisol, glucagon, growth hormone, and catecholamines can result from a relative or absolute lack of insulin. These hormones promote glycogenolysis and gluconeogenesis, which leads to hyperglycemia. Chronic hyperglycemia can then lead to the accumulation of ketone bodies, which can lead to metabolic acidosis and diabetic ketoacidosis (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) This often recurs because of poor adherence to insulin therapy (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Anorexia, nausea, vomiting, polyuria, thirst, stomach pain, and altered mental status are clinical signs of diabetic ketoacidosis (DKA). Intravenous fluid administration, identifying precipitating events, potassium replacement, and insulin administration are recommended therapies (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDiabetic ketoacidosis is the leading cause of death in children and young adults, accounting for approximately 50% of deaths, with half of all deaths occurring in patients under the age of 24 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Treatment of this condition requires a significant amount of resources, with an estimated yearly total cost of \u003cspan\u003e$\u003c/span\u003e2.4\u0026nbsp;billion (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In high-income nations (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), the mortality rate from DKA ranges from less than 1\u0026ndash;4.5%, but in low- and middle-income countries, it ranges from 10\u0026ndash;30% (\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). According to reports, the mortality rate from DKA was approximately 23.6% in 2019 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Age and urban residency were found to be sociodemographic predictors of DKA in previous studies (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Furthermore, Type 1 DM, comorbidity, infection before DKA onset, poor glycemic control, no family history of diabetes mellitus, infection, comorbidities, and stopping medication were all found to be predictors of DKA (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrevious studies have revealed little information about the prevalence of DKA in Ethiopia (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). However, evidence regarding the incidence of DKA and its predictors in Ethiopia has been scarce, especially in the study area. For example, the overall incidence rate of diabetic ketoacidosis was determined to be 2.2 per 100 person-months in a single research carried out at Woldiya Comprehensive Specialized Hospital in northern Ethiopia (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Therefore, this study aimed to assess the incidence of DKA and its predictors among adult patients with diabetes mellitus using a retrospective cohort study in eastern Ethiopia. Understanding the local incidence and specific predictors of DKA within the Ethiopian context is essential for developing targeted prevention strategies, optimizing resource allocation, and ultimately improving patient outcomes.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy setting, design, and period\u003c/h2\u003e\u003cp\u003eA retrospective cohort study was carried out involving adult diabetes mellitus (DM) patients undergoing long-term follow-up from January 1, 2019, to December 31, 2024. Data were collected from public hospitals in the Harari region, located in the Harari regional state approximately 526 kilometers from Addis Ababa, Ethiopia\u0026rsquo;s capital, during the period of January 1 to January 31, 2025. The Harari region has an estimated population of 283,000, with an equal male-to-female ratio(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The region is supported by a total of six hospitals, including two public, two private, one police, and one non-governmental hospital. This study took place in two public hospitals within the region: Hiwot Fana Comprehensive Specialized University Hospital (HFCSUH) and Jugal General Hospital (JGH). HFCSUH, a major teaching hospital with 210 beds, functions as a key referral center serving around 5.8\u0026nbsp;million people in eastern Ethiopia (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In contrast, Jugal General Hospital primarily serves the people of Harar town and has 95 beds. Approximately 1,500 DM patients currently receive routine follow-up care at these two institutions, including 852 patients at HFCSUH and 648 at JGH (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePopulations\u003c/h3\u003e\n\u003cp\u003eThe source population included all adult diabetic patients receiving long-term care at public hospitals in the Harari area. The study population comprised all adult diabetes patients on chronic follow-up at HFCSUH and JGH from January 1, 2019, to December 31, 2024.\u003c/p\u003e\n\u003ch3\u003eEligibility criteria\u003c/h3\u003e\n\u003cp\u003eThe study included all adult diabetic patients diagnosed between January 1, 2019, and December 31, 2024, receiving chronic follow-up treatment at public hospitals of Harari region. However, individuals who were brought in from other facilities or who acquired DKA at the time of their original diabetes diagnosis were not included.\u003c/p\u003e\n\u003ch3\u003eSample size and sampling procedures\u003c/h3\u003e\n\u003cp\u003eThe final sample size of 455 was calculated using the Schoenfeld formula in Stata version 17, based on a power analysis for the Cox proportional hazards model.(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). This was estimated by considering a 31% event probability and an adjusted hazard ratio (AHR) of 0.59 for urban residence, as reported in a previous study (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). We assumed a 95% confidence interval, 80% power, and 20% anticipated withdrawal rate.\u003c/p\u003e\u003cp\u003eAll public hospitals in the Harari region \u0026mdash;namely, HFCSUH and JGH \u0026mdash;were included in the study. Prior to allocating the sample, the number of newly diagnosed diabetes mellitus (DM) patients attending follow-up care at both hospitals during the study period was identified. A total of 1,896 adult DM patients were recorded\u0026mdash;998 from HFCSUH and 898 from JGH\u0026mdash;between January 1, 2019, and December 31, 2024.\u003c/p\u003e\u003cp\u003e The overall sample size of 455 was distributed proportionally between the two hospitals according to the number of diabetic patients receiving follow-up care at each site. A compilation of medical record numbers for adults with diabetes was then created from the diabetic follow-up logbooks of both hospitals. Finally, the required participants were randomly selected using Excel-generated random numbers from this sampling frame.\u003c/p\u003e\n\u003ch3\u003eStudy variables\u003c/h3\u003e\n\u003cp\u003eThe incidence of DKA was the dependent variables, while the independent variables encompassed socio-demographic characteristics \u003cb\u003e(\u003c/b\u003eage, sex, residence, community health insurance), clinical factors \u003cb\u003e(\u003c/b\u003eBMI, family history of diabetes, recent infection, type of diabetes, glycemic control, duration of diabetes, presence of comorbidities, presence of chronic diabetic complications), and treatment factors (frequency of follow-up, type of medication used, treatment duration, and medication non-adherence).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData collection instrument and procedure\u003c/h2\u003e\u003cp\u003eA data extraction format that was modified from pertinent literature was used to gather the data (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The format consisted of five parts: general information, socio-demographic factors, clinical factors, treatment-related factors, and the follow-up form. Initially, all DM patients on long-term follow-up from January 1, 2019, to December 31, 2024, were identified using the diabetes log book. From this group, eligible adult patients were then randomly chosen through Excel-generated random numbers, excluding those who developed diabetic ketoacidosis (DKA) at initial diagnosis and transferred-in cases. Data were extracted from patient cards using a pretested data collection tool. Medical registration numbers were utilized to locate the records reviewed. The data collection was carried out by two BSc public health professionals experienced in data handling, under the supervision of a trained BSc nurse specialized in chronic follow-up and a public health expert with a master's degree.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData quality control\u003c/h3\u003e\n\u003cp\u003eTo ensure data quality, data collectors and supervisors received a one-day training session covering the data extraction process, data collection tools, and the objectives of the study. Prior to the main data collection, a pretest was conducted on 5% (23 participants) of the total sample at HFCSUH. These pretest samples were excluded from the final analysis. Based on the pretest findings, necessary adjustments were made to the data extraction format. One week before data collection began, patient charts were reviewed to confirm the adequacy of the tools, ensure timely completion of the checklists, and verify the completeness of chart data. Continuous supervision and close monitoring were provided by both the principal investigator and the supervisor throughout the data collection period. Daily feedback was given to data collectors to address any issues promptly. All collected data were checked for completeness before analysis. To reduce misclassification bias, consistent criteria were applied for clinical variables with multiple diagnostic definitions. Missing data were managed through a complete case analysis approach.\u003c/p\u003e\n\u003ch3\u003eOperational definitions\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEvent:\u003c/em\u003e\u003c/strong\u003e The occurrence of DKA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCensored:\u003c/em\u003e\u003c/strong\u003eAdults with diabetes who did not experience DKA during the follow-up period (transferred out, died, lost to follow-up, or had not developed DKA by the end of the study).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIncomplete records:\u003c/em\u003e\u003c/strong\u003eThese are charts that do not contain all necessary information for the variables of the date of DM diagnosis, the date DKA occurred, and the follow-up history after diagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDKA status:\u003c/em\u003e\u003c/strong\u003e\u0026apos;Yes\u0026apos; for DKA indicates the occurrence of the first incident of DKA, based on the clinical decisions of the physicians and obtained from the patient\u0026rsquo;s medical records. This status is also validated based on the results of biochemical parameters: serum glucose \u0026gt; 250 mg/dL, acidosis (arterial blood pH \u0026lt; 7.3 and bicarbonate \u0026lt; 15 mEq/L), and urine ketones \u0026ge; 2+ (38).\u0026apos;No\u0026apos; for DKA indicates that the patient did not experience any incidents of DKA during the follow-up period and is considered censored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003cem\u003eMedication non-adherence:\u003c/em\u003e\u003c/strong\u003e Records showing that the patient has ceased (discontinued) taking prescribed anti-diabetic medications (39).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGlycemic control:\u003c/em\u003e\u003c/strong\u003eGood glycemic control is defined as an average fasting blood sugar (FBS) level between 70 to 130 mg/dl or an HbA1c of less than 7%. Poor glycemic control is indicated by an average FBS greater than 130 mg/dl or less than 70 mg/dl, or an HbA1c greater than 7% (40).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDuration of treatment started:\u0026nbsp;\u003c/strong\u003ePatients who started treatment on the same day of DM diagnosis by the doctor are considered immediate users; otherwise, they are considered non-immediate users (41).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAdult\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u003c/em\u003e Patients who are 18 years or older.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData processing and\u0026nbsp;analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing data collection, the data were exported from Kobo Toolbox to Microsoft Excel and subsequently imported into STATA version 17 for further cleaning and analysis. Basic descriptive statistics were performed. Each patient was monitored until the development of diabetic ketoacidosis (DKA) or until censoring, whichever happened first. Survival analysis was utilized as the statistical method to assess the relationship between DKA incidence and its predictors. The probability of remaining free from DKA was calculated in months, based on the time from DM diagnosis to either DKA occurrence or censoring. The incidence rate of DKA was also determined. Life tables were employed to estimate survival probabilities at various time points after DM diagnosis. Additionally, Kaplan-Meier survival curves and log-rank tests were conducted to estimate survival probabilities and to compare survival across different predictor groups statistically.\u003c/p\u003e\n\u003cp\u003eThe Schoenfeld residuals test for the proportional hazards assumption was performed for each individual predictor as well as for global tests (42). All predictors had a P-value greater than 0.05, and the overall global test showed a P-value of 0.134, suggesting that the proportional hazards assumption was not violated. Plots were utilized to assess the predictors in the model, and the variables in the final model displayed parallel curves, indicating proportional hazards among the groups. The presence of multicollinearity was evaluated using the variance inflation factor (VIF), which was 1.81, indicating no significant multicollinearity issues.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA Cox proportional hazards regression model was used to identify predictors. Variables with a p-value less than 0.25 in the bi-variable analysis were considered candidates for the multivariable Cox proportional hazards model. Hazard ratios (HR) along with their respective 95% confidence intervals (CI) were reported to indicate the significance and strength of the relationship with the dependent variable. Variables with a p-value below 0.05 in the multivariable model were considered significant and independently associated with the outcome.\u003c/p\u003e\n\u003cp\u003eThe overall model fit was assessed using the Cox-Snell residual plot. The close correspondence between the Cox-Snell residual line and the 45-degree cumulative hazard line suggested a good model fit, as the residual line closely tracked the bisector with minimal deviations (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical clearance and approval were granted by the Institutional Health Research Ethics Review Committee (IHRERC) of Haramaya University College of Health and Medical Sciences. A cooperation letter was secured from Haramaya University College of Health and Medical Sciences addressed to the respective hospitals. Informed, voluntary, written, and signed consent was obtained from the heads of the hospitals. Following ethical approval, data were collected from patient charts using a checklist, with codes applied to ensure confidentiality. The completed checklists were stored securely in a locked location. The entered data were password-protected on the computer. Access to the data was restricted to the principal investigator.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSocio-demographic factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 455 patient cards reviewed, 446 were part of the final analysis, whereas 9 patients (2%) were excluded due to incomplete information and lack of follow-up history. Among the total data, 259 patients (58.07%) were from HFCSUH and 187 (41.93%) from Jugal General Hospital. Of the 446 adults with diabetes, 49.78% were female, and 206 patients (46.19%) lived in rural areas. The median age at diabetes diagnosis was 46 years, with an interquartile range (IQR) of 27 years. Additionally, 158 patients (35.43%) did not have community-based health insurance. (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical and treatment characteristics of diabetes mellitus patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the study, three-fourths (75.78%) of the diabetes patients, had T2DM. The majority, 391 patients (90.51%), reported no family history of diabetes. Among the participants, 157 (35.20%) had an infection, and 201 (45.07%) had uncontrolled blood sugar levels. At baseline, 253 patients (56.73%) had at least one comorbidity, and 72 patients (17.38%) had more than one. Cardiovascular disorders were the most prevalent comorbidities, affecting 156 patients (34.98%), of which hypertension accounts for 135 cases (86.53%). Regarding chronic complications of diabetes, 12 adults (2.69%) experienced diabetic neuropathy, while 11 adults (2.47%) had diabetic nephropathy. Most patients, 284 (63.68%), had a normal body mass index (BMI), and 157 (35.20%) had irregular follow-up appointments. Additionally, 306 patients (68.61%) were prescribed oral hypoglycemic agents at diagnosis, and 93 (20.85%) of the adult diabetes patients were noncompliant with their medication (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIncidence of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ediabetic ketoacidosis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 446 diabetes patients monitored over five years, 110 (24.66%) developed diabetic ketoacidosis, with a 95% CI of 20.87% to 28.88%. The overall incidence rate of DKA during a total of 10,037 person-months of observation was 1.1 cases per 100 person-months (95% CI: 0.9-1.3), with rates of 2.7 per 100 person-months for type 1 diabetes mellitus and 0.7 per 100 person-months for type 2 diabetes mellitus. The incidence rate was particularly high among younger individuals (aged 18–44) and rural residents, both showing a rate of 1.7 per 100 person-months of observation (95% CI: 1.3–2.1).\u0026nbsp;Throughout the follow-up period, the highest incidence of DKA occurred within the first 0 to 6 months, while the lowest incidence was noted during the 36 to 42 months follow-up interval. (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Kaplan–Meier estimate indicated a high probability of DKA-free survival among adult diabetes patients at 1.2 months of observation, recorded at 99.55%. However, this probability gradually declined with longer follow-up periods, dropping to a minimum of 58.35% by 59.87 months of observation. The median DKA-free survival time was undefined, as more than 50% of the patients remained free from DKA throughout the follow-up period (\u003cstrong\u003eFigure 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictors of DKA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the bi-variable Cox regression analysis, twelve variables were identified as being associated with DKA at a significance level of p ≤ 0.25. These factors included community-based health insurance (CBHI), residence, age, medication non-compliance, glycemic control, body mass index (BMI), type of diabetes mellitus, trauma, drugs, acute febrile illness (AFI), respiratory infections, and urinary tract infections (UTIs). In the multivariable Cox regression analysis, five variables were identified as predictors of DKA. These were Medication non-compliance, Glycemic control, BMI, AFI, and UTI.\u003c/p\u003e\n\u003cp\u003eAccordingly, DM patients with medication non-adherence had a 2-fold higher probability of DKA than patients with medication adherence (AHR: 2.27, 95% CI: 1.46, 3.54). The risk of DKA among DM patients was 2.8 times higher among patients without glycemic control as compared to those with glycemic control (AHR: 2.79, 95% CI: 1.72, 4.54).\u003c/p\u003e\n\u003cp\u003eRegarding BMI, DM patients who were overweight had 2.23 times (AHR: 2.23, 95% CI: 1.45, 3.42) higher hazard of developing DKA compared to those with normal BMI. Patients who had experienced AFI were 2.15 times more likely to develop DKA compared to those who did not experience AFI (AHR: 2.15, 95% CI: 1.51, 3.07). The hazard of DKA among DM patients was 3 times higher among patients with UTI as compared to those without UTI (AHR: 3.04, 95% CI: 1.99, 4.64) (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, 24.66% (95% CI: 20.87\u0026ndash;28.88%) of patients with diabetes mellitus (DM) experienced diabetic ketoacidosis (DKA). The overall incidence rate of DKA was 1.1 cases per 100 person-months (95% CI: 0.9\u0026ndash;1.3), equivalent to 13.3 cases per 100 person-years (95% CI: 11.1\u0026ndash;16.1). Among these, the incidence rates were 2.7 and 0.7 per 100 person-months for patients with type 1 diabetes (T1DM) and type 2 diabetes (T2DM), respectively. Factors such as medication non-compliance, glycemic control, body mass index (BMI), acute febrile illness (AFI), and urinary tract infection (UTI) were significantly linked to an increased risk of developing DKA.\u003c/p\u003e\u003cp\u003eThe incidence rate of DKA in this study was lower than the study in Woldiya, Ethiopia (2.2 per 100 PM (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). This could be due to differences in types of DM and residence. In our study, 75.78% of individuals had Type 2 diabetes, while in Woldiya, 76.15% were diagnosed with Type 1 diabetes. Additionally, 53.81% of the participants in our study lived in urban areas, whereas 55.4% of those in the Woldiya research were rural residents. The incidence rate of DKA observed in this study was greater than that reported in a study conducted in Western Australia (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e),which had a rate of 0.04 per 100 PYs in China (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) with a rate of 1.21 per 100 person-years, in Spain (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)Which reported 0.06 per 100 PYs, and a study performed in the USA, which indicates a rate of 0.17 per 100 PYs. One potential reason for this variation could be the differences in sample size and duration of follow-up; for example, a study conducted in China tracked participants over a twelve-year period. Moreover, the discrepancy may stem from differences in study design, with the Western Australia research using a prospective cohort approach involving 1,724 participants, while the current study applied a retrospective cohort design. Additionally, variations in socio-economic and socio-cultural factors affecting health-seeking behaviors might also contribute to this difference.\u003c/p\u003e\u003cp\u003eMedication non-adherence was positively associated with DKA in this study. This finding aligns with a previous study conducted in the Amhara region, Ethiopia (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)and Cameroon (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), indicating a persistent relationship between medication non-compliance and increased risk of DKA. The increased risk is probably attributed to the direct effect of non-compliance on blood glucose control. When patients fail to adhere to their medication regimen, their blood glucose levels can rise substantially, leading to a greater chance of developing diabetic ketoacidosis (DKA), a severe complication marked by elevated blood sugar and ketone levels (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePatients with poor glycemic control were positively associated with DKA. These results are consistent with research done in Woldiya, Ethiopia (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), studies done at Debre Markos Referral Hospital (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), and Ayder Referral Hospital (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). This might be because poor glycemic control is one of the markers of DKA (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Poor glycemic control leads to consistently high blood glucose levels. When blood glucose levels rise significantly, it can result in osmotic diuresis, causing dehydration and increasing the risk of DKA. Elevated glucose levels also promote the breakdown of fat for energy, leading to the production of ketones, which can cause ketoacidosis (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, DM patients who were overweight had a higher likelihood of developing diabetic ketoacidosis (DKA). This finding is supported by research from South West Ethiopia (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) and Western Australia (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Being overweight may contribute to insulin resistance and increased levels of fatty acids. Elevated free fatty acids can promote insulin resistance and stimulate ketogenesis, which plays a critical role in the development of DKA (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Although DKA typically presents in lean individuals with Type 1 diabetes, there is an epidemiological trend showing a rise in DKA cases among people with Type 2 diabetes (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHaving AFI was significantly associated with DKA among DM patients. These results are consistent with research done in Woldiya, Ethiopia (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), in South Africa(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) and in Dilla University Referral Hospital (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). This might be due to increased insulin resistance, the release of counter-regulatory hormones, and dehydration in Acute febrile illnesses (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDM Patients with UTI were more prone to develop DKA than those without the condition. This aligns with findings from studies conducted in Jimma, Ethiopia (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e); Bahir Dar, Ethiopia (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e); Cameroon (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), and Iraq (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e), all of which identified acute recent illness as a significant predictor of DKA. The likely reason for this association is that UTI can lead to physiological stress, which may increase insulin resistance, and it often causes symptoms such as fever and increased urination, which can lead to dehydration (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). This dehydration can ultimately contribute to the onset of DKA. These findings highlight the need for further research to enhance understanding of the predictors of diabetic ketoacidosis and to assess its implications for health policy.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and limitations of this study\u003c/h2\u003e\u003cp\u003eThis study analyzed five years of follow-up data, offering a more thorough insight into the incidence of DKA over time. However, the retrospective study design restricted the ability to include all potential factors influencing patients' DKA status. The diagnosis of DKA was based on physicians\u0026rsquo; clinical judgment, which may not always be fully reliable, raising the possibility of misclassification and documentation bias that could impact the results' validity. Furthermore, the study lacked standardized laboratory confirmation for DKA, may have underreported comorbidities or infections, was unable to account for time-varying exposures, and its findings may have limited applicability outside of public hospital patient populations.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, diabetic ketoacidosis (DKA) was observed in one in every four patients with diabetes mellitus receiving care at public hospitals. A history of medication non-adherence, poor glycemic control, being overweight, acute febrile illness (AFI), and urinary tract infection (UTI) were significantly linked to an increased risk of developing DKA. We recommend placing particular focus on follow-up care for diabetic patients presenting these risk factors to help lower the incidence of DKA. Lastly, we encourage conducting further prospective follow-up research that incorporates time-dependent covariates for variables that may change over time and addresses any missing factors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki. Approval for studies involving humans was granted by the Institutional Health Research Ethics Review Committee of Haramaya University College of Health and Medical Sciences (Ref. No. IHRERC/149/2024). The research adhered to local legislation and institutional requirements. The ethics committee waived the requirement for written informed consent from participants or their legal guardians/next of kin due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used to support the findings of this study are available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;author(s)\u0026nbsp;received\u0026nbsp;no\u0026nbsp;financial\u0026nbsp;assistance\u0026nbsp;for\u0026nbsp;the\u0026nbsp;research,\u0026nbsp;authorship,\u0026nbsp;or\u0026nbsp;publication\u0026nbsp;of\u0026nbsp;this\u0026nbsp;article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) have declared no potential conflicts of interest in the research, authorship, and/or publication of this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp id=\"_Toc137590657\"\u003eAll authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or all these areas; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our gratitude to the administrative staff and card room workers at the Harari region public hospital for their collaboration. Additionally, we appreciate the dedication of the data collectors and supervisors throughout the data collection process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaynes HJJdm. Classification, pathophysiology, diagnosis and management of diabetes mellitus. 2015;6(5):1-9.\u003c/li\u003e\n\u003cli\u003eBenoit SRJMM, report mw. Trends in diabetic ketoacidosis hospitalizations and in-hospital mortality\u0026mdash;United States, 2000\u0026ndash;2014. 2018;67.\u003c/li\u003e\n\u003cli\u003eDesse TA, Eshetie TC, Gudina EKJBrn. Predictors and treatment outcome of hyperglycemic emergencies at Jimma University Specialized Hospital, southwest Ethiopia. 2015;8:1-8.\u003c/li\u003e\n\u003cli\u003eNewton CA, Raskin PJAoim. Diabetic ketoacidosis in type 1 and type 2 diabetes mellitus: clinical and biochemical differences. 2004;164(17):1925-31.\u003c/li\u003e\n\u003cli\u003eDhatariya K, Savage M, Sampson M, Matfin G, Scott AJE, Guide MMEACs. 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Diabetes care. 2017;40(9):1249-55.\u003c/li\u003e\n\u003cli\u003eYahaya JJ, Doya IF, Morgan ED, Ngaiza AI, Bintabara D. Poor glycemic control and associated factors among patients with type 2 diabetes mellitus: A cross-sectional study. Scientific Reports. 2023;13(1):9673.\u003c/li\u003e\n\u003cli\u003eGebre BB, Assefa ZM. Magnitude and associated factors of diabetic complication among diabetic patients attending Gurage zone hospitals, South West Ethiopia. BMC research notes. 2019;12:1-6.\u003c/li\u003e\n\u003cli\u003eKlein S, Gastaldelli A, Yki-J\u0026auml;rvinen H, Scherer PEJCm. Why does obesity cause diabetes? 2022;34(1):11-20.\u003c/li\u003e\n\u003cli\u003eWang Y, Desai M, Ryan PB, DeFalco FJ, Schuemie MJ, Stang PE, et al. Incidence of diabetic ketoacidosis among patients with type 2 diabetes mellitus treated with SGLT2 inhibitors and other antihyperglycemic agents. Diabetes research and clinical practice. 2017;128:83-90.\u003c/li\u003e\n\u003cli\u003eNdebele NF, Naidoo M. The management of diabetic ketoacidosis at a rural regional hospital in KwaZulu-Natal. African Journal of Primary Health Care and Family Medicine. 2018;10(1):1-6.\u003c/li\u003e\n\u003cli\u003eEskeziya A, Girma Z, Mandefreo B, Haftu A. Prevalence of Diabetic Keto Acidosis and Associated Factors among Newly Diagnosed Patients with Type One Diabetic Mellitus at Dilla University Referral Hospital, September 9th/2017\u0026ndash;May 30th/2019: South Ethiopia; Crossectional Study. J Healthcare. 2020;3(1):33-8.\u003c/li\u003e\n\u003cli\u003eBlanchard F, Charbit J, Van der Meersch G, Popoff B, Picod A, Cohen R, et al. Early sepsis markers in patients admitted to intensive care unit with moderate-to-severe diabetic ketoacidosis. Annals of intensive care. 2020;10:1-10.\u003c/li\u003e\n\u003cli\u003eDesse TA, Eshetie TC, Gudina EK. Predictors and treatment outcome of hyperglycemic emergencies at Jimma University Specialized Hospital, southwest Ethiopia. BMC research notes. 2015;8:1-8.\u003c/li\u003e\n\u003cli\u003eAbate MD, Semachew A, Emishaw S, Meseret F, Azmeraw M, Algaw D, et al. Incidence and predictors of hyperglycemic emergencies among adult diabetic patients in Bahir Dar city public hospitals, Northwest Ethiopia, 2021: A multicenter retrospective follow-up study. Frontiers in Public Health. 2023;11:1116713.\u003c/li\u003e\n\u003cli\u003eMansour A, Abdu-Alla M. Predictors of diabetic ketoacidosis among patients with type 1 diabetes mellitus seen in the emergency unit. British Journal of Medicine and Medical Research. 2016;11(10):1-12.\u003c/li\u003e\n\u003cli\u003eNitzan O, Elias M, Chazan B, Saliba W. Urinary tract infections in patients with type 2 diabetes mellitus: review of prevalence, diagnosis, and management. Diabetes, metabolic syndrome and obesity: targets and therapy. 2015:129-36.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1:\u0026nbsp;Incidence of DKA and socio-demographic characteristics of diabetes patients at public hospitals of Harari region, Eastern Ethiopia, 2025. (N=446)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"666\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 237px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; DKA Status\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\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 valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeveloped DKA(n=110)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCensored\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=336)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eHealth facility\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eHFCSUH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e259(58.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eJGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e187(41.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eAge at diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e18-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e195(43.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e45-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e193(43.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026ge; 65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e58(13.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e224(50.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e222(49.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e240(53.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e206(46.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eCBHI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e288(64.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e158(35.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCBHI, community-based health insurance; HFCSUH, Hiwot Fana Comprehensive Specialized University Hospital; JGH, Jugal General Hospital\u003c/p\u003e\n\u003cp\u003eTable 2: Clinical and treatment-related results of adult patients with diabetes at public hospitals of Harari region, Eastern Ethiopia, 2025. (N=446)\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"678\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eType of DM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eType2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e338(75.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eType1DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e108(24.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eDM duration\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003e\u0026lt;3 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e347(77.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003e\u0026ge; 3 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e99(22.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eFamily history of DM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e391(90.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e41(9.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eGlycemic control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eGood glycemic control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e245(54.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003ePoor glycemic control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e201(45.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eTrauma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e430(96.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e16(3.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eBody mass index status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e284(63.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e124(27.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e24(5.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e14(3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eAcute infection \u0026nbsp; \u0026nbsp; \u0026nbsp;\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 \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eUrinary tract infection\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e63(14.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eRespiratory infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e46(10.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eGastrointestinal infection\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e25(5.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eAcute febrile illness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e14(3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eMyocardial infarction\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e10(2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eOthers*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e17(3.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eComorbidities\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 \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 \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eCardiovascular disorder\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e156(34.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eRenal disease\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e27(6.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eChronic respiratory disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e14(3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eLiver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e8(1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eHIV/AIDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e8(1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eBenign prostate hyperplasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e10(2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003ePsychiatric disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e5(1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eOthers**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e18(4.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eMore than one comorbidity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e47(10.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eChronic DM complication\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eDiabetic neuropathy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e12(2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eDiabetic nephropathy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e11(2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eDiabetic retinopathy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e8(1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eDiabetic foot ulcers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e4(0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eType of drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eOral hypoglycemic agent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e306(68.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e131(29.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eOral and Insulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e9(2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eMedication non-adherence\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e353(79.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e93(20.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eFollow up frequency\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eRegular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e289(64.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eIrregular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e157(35.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eDuration of treatment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eImmediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e448(94.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eNot immediate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e24(5.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Others include osteoarthritis, acute otitis media, sexually transmitted infections, necrotizing fasciitis, periodontitis, pyomyositis, cellulitis, and carbuncle.\u003c/p\u003e\n\u003cp\u003e**Others include rectal cancer, dyslipidemia, hypothyroidism, epilepsy, Parkinson\u0026apos;s disease, hyperthyroidism, goiter, and cholelithiasis\u003c/p\u003e\n\u003cp\u003eTable 3: Life table showing the survival to develop DKA among adult diabetic patients at public hospitals of Harari region, Eastern Ethiopia, 2025. (N=446)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterval in months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients at risk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;N\u003cu\u003eo\u003c/u\u003e of DKA cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCensored\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Cumulative Survival\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;SD. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; L \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;U\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e(0 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.0128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.0551 \u0026nbsp; 0.1061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e(6 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.1344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.0169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.1048 \u0026nbsp; 0.1716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e(12 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.2099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.0216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.1711 \u0026nbsp; 0.2559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e(18 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.2345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.0231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.1928 \u0026nbsp; 0.2835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e(24 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.2897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.0268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.2409 \u0026nbsp; 0.3459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e(30 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.3272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.0294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.2733 \u0026nbsp; 0.3886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e(36 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 42]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.3415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.0305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.2855 \u0026nbsp; 0.4049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e(42 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 48]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.3771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.0336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.3151 \u0026nbsp; 0.4467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e(48 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.4160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.0383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.3453 \u0026nbsp; 0.4950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e(54 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.4160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.0383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.3453 \u0026nbsp; 0.4950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4. Bivariate and multivariate Cox regression analysis of predictors of DKA among DM patients at public hospitals in the Harari region, East Ethiopia, 2025.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"730\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDKA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCensored (n=336)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u0026nbsp;\u003c/strong\u003e(\u003cstrong\u003en=110)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eCBHI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e3.09 (2.12- 4.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.45 (.95-\u0026nbsp;2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e135 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e2.54(1.72-3.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.39 (.90-2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e201 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.95(0.94-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.99(0.97-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMedication non-adherence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e299 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e37 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e5.23(3.59-7.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.27 (1.46-3.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eglycemic control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e218 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eUncontrolled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e118 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e5.01(3.24-7.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.79 (1.72-4.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e233 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\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 valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e2.71 (1.16- 6.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.10 (0.45-2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.823 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e76 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e2.53 (1.71- 3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.23(1.45-3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e19 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.37(0.55- 3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.44(0.90-6.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTypes of DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eType 2 DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e286 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eType 1 DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e50 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e3.97(2.73- 5.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.55 (.65-3.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTrauma\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e327 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e2.92(1.35- 6.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.14(.90-5.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.084 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eDrugs\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eOral\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e261 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eInsulin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e69 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e3.54 (2.41-5.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.45 (.68-3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.334 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eOral and Insulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.97(.61-6.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.40(.41- 4.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.588 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAFI\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e2.94(1.43-6.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.89 (1.33- 6.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e330 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eRespiratory infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e26 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e2.23(1.37-3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.52 (.89-2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.127\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e310 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eUTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e303 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e33 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e3.04(1.99-4.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.53(1.59- 4.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001 \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCBHI, community-based health insurance; BMI, body mass index; AFI, Acute febrile illness; UTI, Urinary tract infection\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Incidence, diabetic ketoacidosis, predictors, diabetes mellitus, eastern Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-7278066/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7278066/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDiabetic ketoacidosis (DKA) is the most prevalent and serious acute complication of diabetes mellitus. Over the past decade, the global incidence of DKA hospitalizations has risen, with recent studies reporting a 55% increase. Therefore, this study aimed to assess the incidence and identify predictors of DKA among adult patients with diabetes in eastern Ethiopia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eAn institution-based retrospective cohort study was conducted in public hospitals in the Harari region of Ethiopia from January 1, 2019, to December 31, 2024, among 455 adults with diabetes mellitus. Data collection was performed using the Kobo toolbox, and analysis was carried out using STATA software version 17. The Cox proportional hazards regression model was applied to identify predictors of DKA. Adjusted hazard ratios (AHR) with 95% confidence intervals (CI) and corresponding p-values were computed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOut of the 446 patients included in the study, 110 (24.66%), 95%CI (20.87%-28.88%) developed diabetic ketoacidosis. The incidence rate of DKA was 1.1 cases per 100 person-months (95% CI: 0.9\u0026ndash;1.3), with rates of 2.7 per 100 person-months for T1DM and 0.7 per 100 person-months for T2DM. Medication non-adherence (AHR: 2.27, 95% CI: 1.46, 3.54), poor glycemic control (AHR: 2.79, 95% CI: 1.72, 4.54), acute febrile illness (AHR: 2.15, 95% CI: 1.51, 3.07), urinary tract infection (AHR: 3.04, 95% CI: 1.99, 4.64) and overweight (AHR: 2.23, 95% CI: 1.45, 3.42) were predictors significantly associated with DKA.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe study revealed that diabetic ketoacidosis occurred in one out of four diabetic patients. Factors such as medication non-adherence, poor glycemic control, overweight, acute febrile illness, and urinary tract infections significantly increased the risk of DKA. Therefore, targeted follow-up care is essential for diabetic patients with these identified predictors to reduce the incidence of DKA.\u003c/p\u003e","manuscriptTitle":"Incidence and predictors of diabetic ketoacidosis among adult type 1 and type 2 diabetes mellitus patients at public hospitals in Harari Region, eastern Ethiopia: A retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 11:05:51","doi":"10.21203/rs.3.rs-7278066/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"208057560050486848876790582015886784024","date":"2025-09-16T11:56:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318242441628161366777161694157454441233","date":"2025-09-04T14:05:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-04T08:30:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-03T06:23:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-14T08:42:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-13T13:48:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2025-08-13T13:45:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4043960e-38d7-404d-b712-307a99d1b6ba","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-11T11:05:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 11:05:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7278066","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7278066","identity":"rs-7278066","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}