Frequency of Diabetes ketoacidosis (DKA) occurrence among adult Diabetes Mellitus patients in Ethiopia: A Negative Binomial Regression analysis

Frequency of Diabetes ketoacidosis (DKA) occurrence among adult Diabetes Mellitus patients in Ethiopia: A Negative Binomial Regression analysis | 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 Frequency of Diabetes ketoacidosis (DKA) occurrence among adult Diabetes Mellitus patients in Ethiopia: A Negative Binomial Regression analysis Berihun Bantie, Gebrie kassaw Yirga, Moges Wubneh Abate, Adane Birhanu Nigat, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7316884/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction Diabetes ketoacidosis (DKA) is a life-threating acute complication of Diabetes Mellitus (DM) characterized by the triad of hyperglycemic crisis, ketosis, and acidosis. The frequency of DKA occurrences is an important indicator of both the adherence to management protocols and the quality of life of DM patients. However, information regarding the frequency and predictors of DKA among adult DM patients is limited in Ethiopia. Therefore, this study is aimed at investigating the frequency and associated factors of DKA among DM patients in northwest Ethiopia. Methods An institutional-based retrospective cohort study was conducted at Debre Tabor Comprehensive Specialized Hospital among 370 randomly selected patients. Data were extracted from patients' medical records, entered with Epidata 4.6 software, and exported to Stata 16.0 software for analysis. A zero-inflated negative binomial regression model was fitted to identify determinants of recurrent episodes of DKA. Adjusted incidence rate ratios (IRRs) with 95% confidence intervals were used to declare statistical significance. Results I n the final analysis, all 370 (100%) study participants were included, with over half (208, or 56.2%) being type II DM patients. Throughout the three-year follow-up period, approximately 76.2% (95% CI: 71.9–80.0) of participants developed DKA, and among them, more than three-quarters (75.2%) experienced recurrent DKA attacks. Residing in a rural area (AIRR = 1.48, 95% CI: 1.26–1.74), being unable to read and write (AIRR = 1.52, 95% CI: 1.13–2.04), having primary-level educational status (AIRR = 1.55, 95% CI: 1.18–2.05), having type I DM (AIRR = 1.62, 95% CI: 1.28–2.05), not being enrolled in health insurance (AIRR = 1.26, 95% CI: 1.10–1.48), and having other comorbidities (AIRR = 1.54, 95% CI: 1.29–1.81) increase the frequency of DKA attacks. Conclusion and Recommendation: In this study, a high frequency of DKA was observed, with over three-quarters of DKA patients experiencing recurrent attacks. Rural residence, low educational attainment, type I DM, lack of health insurance enrollment, and comorbidities were identified as contributing factors. Stakeholders are urged to boost community engagement in health insurance, enhance socio-economic status, and prioritize type II DM patients and those with comorbidities. Frequency Diabetes ketoacidosis Risk factors Adults Debre Tabor Comprehensive Specialized Hospital Figures Figure 1 1. Introduction Diabetes ketoacidosis (DKA) is a life-threatening acute complication of diabetes mellitus (DM) characterized by the triad of hyperglycemic crisis, ketosis, and acidosis[ 1 , 2 ]. Although DKA has been reported to affect type 1 diabetes mellitus, patients with type 2 diabetes are also at risk during the catabolic stress of acute illnesses such as trauma, surgery, or infections[ 3 , 4 ]. It has been reported that DKA can be present in 25–30% of type 1 diabetes cases at onset and from 4 to 29% in youth with type-2 diabetes[ 5 ]. In the last two decades, both the incidence and the frequency of DKA are rising [ 6 , 7 ]. In a setting where there is poor health system structure like Ethiopia, diabetes mellitus patients would have bouts of recurrent DKA attack ( greater than one episodes of DKA attack)[ 8 – 10 ]. The occurrence of recurrent diabetic ketoacidosis in diabetes mellitus patients highly depends on the socioeconomic conditions and the availability of medical services in the community. In this regard, omission of and/or inadequate insulin therapy due to transport problem or cost, presence of comorbidities, infection, alcohol/ other substance abuse, cerebrovascular accident, trauma and possible thyrotoxicosis were the main precipitating factors of DKA[ 1 , 11 – 17 ]. DKA in turn is the leading cause of mortality among DM patients, particularly in low income countries including Ethiopia [ 12 , 15 , 18 – 20 ]. With this aspect, the mortality rate in patients with diabetic ketoacidosis (DKA) is < 5% in experienced centers of America and Europe[ 4 ], whereas the mortality rate of patients in Africa is unexpectedly high, ranging from 6 to 30% [ 12 , 15 , 20 ] In Ethiopia, the mortality rate of DKA is high and highly varied across institutions [ 13 , 21 – 23 ]. The high mortality rate in Africa may be partially attributed to poor adherence to diabetic medications and the insufficient availability of medication supplies. DKA is the main reason for the hospitalization of patients with DM, which may indirectly increases the health care cost of hospitalization and mortality rate of the patient[ 24 , 25 ]. Hospital admissions due to this problem constitute a serious economic burden on the healthcare system of particularly low- and -middle income countries. As the frequency of DKA attack is increasing, significant resources are spent on the cost of hospitalization. For instance, in United States of America (USA), DKA episodes represent > 1 of every 4 USD spent on direct medical care for adult patients with type 1 diabetes and 1 of every 2 USD in patients experiencing multiple episodes[ 26 ]. Recurrent episodes of diabetic ketoacidosis (DKA) not only increase the risk of mortality but also significantly affect the quality of life for both individuals with diabetes and their families. In order to mitigate the detrimental effects of DKA, various strategies have been designed and implemented, including education on diabetes self-management options, improving the health service diabetes management system, and developing contextual DKA treatment guidelines [ 27 ]. In this regard, the Federal Ministry of Health (FMOH) of Ethiopia also developed a National Strategic Action plan (NSAP) for four priority Non-Communicable Diseases (NCD), including Diabetes mellitus[ 22 ]. Despite all the efforts made previously, the number of DKA episodes is increasing, becoming a major socioeconomic burden and health threat in Ethiopia. Additionally, although numerous studies have been undertaken in Ethiopia, the majority of them assess the proportion and treatment outcomes of DKA among diabetes mellitus patients[ 10 , 23 ]. Nevertheless, evidence on the frequency of DKA and its predictors among both type I and Type II diabetes mellitus patients is very limited. Assessing the number of DKA attacks and its factors in DM patients plays a paramount role in providing targeted interventions that would prevent further risks and associated healthcare costs and mortality rates. 2. Methods and Materials 2.1 Study Design and Setting An institutional-based retrospective follow-up study was conducted in northwest Ethiopia from September 2020 to June 2023. The study took place at Debre Tabor Comprehensive Specialized Hospital (DTCSH), situated in northwest Ethiopia. The hospital provides service to an estimated population of five million people residing in the surrounding areas. 2.2 Study Participants Adult DM patients aged > 15 years and who started a diabetic follow-up from September 2020 to June 2023 were included in the study. Adult DM patients with unknown dates of diagnosis were excluded from the study 2.3. Sample size determination and sampling technique The sample size for this study was determined using EPINFO version 7.2.3.1 software using parameters like 80% power, 95% confidence level, 10% incompleteness of charts, a 1 to 1 ratio of exposed to un-exposed individuals, and 39.3% and 24.5% incidence of DKA among exposed and non-exposed groups, respectively, in a study conducted at Bahirdar city referral hospitals [ 28 ]. Study participants were randomly selected from follow-up records (Sept 2020–June 2023) using computer-generated sampling technique. 2.4. Study variable The main outcome variable for this study is the number of DKA attacks per patient’s follow-up time. Predictor variables include socio-demographic factors (age, sex, educational status, occupation, residence, and family size), as well as clinical and treatment-related characteristics such as type and duration of diabetes mellitus, presence of comorbidities and infections, types of infection, and medication adherence. 2.5. Operational definitions Diabetes ketoacidosis (DKA) is defined as the occurrence of a marked elevation of random blood glucose level (RBS > 250 mg/dl), ketonuria ( ≥ + 2), and clinical features of DKA (frequent urination, thirst, abdominal pain, dehydration, and/or fruit odor breath) [ 29 , 30 ]. The presence of comorbidities is defined as the occurrence of diabetes mellitus with other chronic medical illnesses. Incompleteness of charts was considered yes when variables like the diagnosis date of DM and age of the patient were missed in the medical record of patients 2.6. Data Collection Procedures The data abstraction tool was developed after reviewing various related literature and was pretested by randomly selecting 18 medical records. The tool encompasses socio-demographic, clinical, and therapeutic characteristics of individuals with DM. Subsequently, necessary data were extracted from patients' charts by three trained Bachelor of Science (BSC) nursing professionals. To ensure data quality, one-day training was provided to both the data collectors and supervisors on the study's purpose, how to extract relevant data, and how to maintain patient information confidentiality to ensure data quality. The principal investigator also monitored the data collection process. 2.7. Data processing and analysis The data were entered into Epi_data version 4.6 and exported to STATA 14.2 software for analysis. descriptive statistics were applied, with continuous variables summarized using either mean and standard deviation (SD) or median and interquartile range (IQR), and frequency with percentage for categorical variables, respectively. Given that the outcome variable—DKA episode frequency—is count data, a Poisson regression model was employed to identify its predictors. However, to run a Poisson regression model, the data should not be over dispersed, or the mean and variance should be equal. In the current data, the assumption is violated, and the data were over-dispersed (deviance/degree of freedom = 1.489). To handle over-dispersed; we have considered another extension of Poisson regression models, namely zero-inflated Poisson (ZIP), negative binomial (NB), and zero-inflated negative binomial (ZINB) regression models. The deviance, Akaike’s Information Criterion (AIC), and Bayesian Information Criterion (BIC) values were used to select the best-fitting model ( Table 1 ). In this aspect, the zero-inflated negative binomial regression model was chosen as the best-fitting model. The omnibus test yielded p < 0.001, indicating a statistically significant overall model. Variables with a p-value of < 0.25 in the bivariable analysis including age, sex, educational status, residence, being a member of health insurance clinical, Type of DM, presence of comorbidities, and presence of infections were fitted to the multivariable zero-inflated negative binomial regression model. The incidence rate ratio (IRR) with a 95% confidence interval was used to declare statistical significance. Variables with p-values less than 0.05 at the 95% confidence interval were considered a statistically significant factor affecting the number of DKA attacks among adult DM patients. Table 1 Model comparison to select the best models to determine the predictors of frequency of DKA in Debre Tabor Comprehensive Specialized Hospital, 2024 (N = 370) S.no Model Deviance AIC BIC 1. Poisson regression model 1,326.06 1346.06 1386.19 2. Zero-inflated Poisson regression 1284. 16 1310 .16 1361.03 3. Negative Binomial regression model 1321.88 1343.89 1386.94 4. Zero –inflated negative binomial regression model 1284.16 1308.14 1355.12 Note: AIC – Akaike’s Information Criterion, BIC- Bayesian Information Criterion 2.8. Ethical considerations An ethical approval letter was obtained from the Ethical Review Committee (ERC) of College of Health Science, Debre Tabor University. Due to the study’s retrospective nature, informed consent was waived by the ERC of college of health sciences with the protocol number Dtu/RP/206/16. Instead, a supportive letter was obtained from the hospital to ensure the use of patients' charts to obtain relevant data for the study. During data collection and entry, patient identifiers such as the patient's medical registration number (MRN) were replaced by new identification numbers or codes. Furthermore, all ethical methods were employed in accordance with relevant guidelines and regulations. 3. Result 3.1. Socio-demographic characteristics of the study participants A total of 370 (100%) study participants were included in the final analysis. Of the total, more than half (216 or 58.38%) were males. The mean age of the study participants was 40 with the standard deviation of ± 14.14 years. Additionally, nearly half (183 or 49.46%) of the study participants live in rural areas ( Table 2 ). Table 2 Baseline socio-demographic characteristics of study participants in Debre Tabor Comprehensive Specialized Hospital, 2024 (N = 370) Variable Category Frequency % Sex Male 216 58.4 Female 154 41.6 Age Age ≤ 40 165 44.4 Age > 40 205 55.6 Mean age = 40, SD (± 14.14) Educational status Not have formal education 111 30.8 Primary 74 20.0 Secondary 122 32.98 Tertiary and above 60 16.2 Marital status Married 238 64.3 Not married 132 33.7 Occupation status Employed 140 37.8 Non- employed 230 62.2 Member of health insurance Yes 197 53.3 No 173 47.7 3.2. Baseline clinical characteristics of the study participants Considering the clinical condition of the participants, more than half (208, or 56.2%) of them were type II DM patients. The median duration of DM for the study participants was 3 years, with an interquartile range (IQR) of 0 to 10 years. More than three-fourths (282, or 76.2%) of DM patients experienced at least one DKA event over the course of the 3-year follow-up period. The major precipitating factor for the incidence of DKA was infection, which accounts for about 36.7%, followed by inadequate anti-diabetes medication therapy. Additionally, more than one-third (131, or 35.5%) of the study participants had another chronic comorbidity. The mean plasma blood glucose level was 409.02 ± 147.26 at the time of diagnosis and 244.15 ± 100.10 at the end of follow-up ( Table 3 ). Table 3 Baseline and follow-up clinical characteristics adult DM patients in Debre Tabor Comprehensive Specialized Hospital, Northwest Ethiopia 2024 (N = 370) Variable Category Frequency % Type of DM Type I 162 43.8 Type II 208 56.2 Duration of DM ≤ 5 years 224 60.5 >5 years 146 39.5 Median duration of DM = 3 years (IQR = 10 − 0 years) Plasma glucose level At admission Mean = 409.02 (SD ± 147.2) At the end of follow-up Mean = 244.15 ( SD ± 100.10) Develop DKA Yes 282 76.2 No 88 23.8 Precipitating factor Infection 137 37.3 Inadequate/missed diabetes medication 70 18.9 Newly diagnosed 53 14.3 Surgery 14 3.8 Others 8 2.2 Presence of comorbidity Yes 131 35.4 No 239 64.6 Type of comorbidity Hypertension 42 32.1 Chronic heart diseases 32 24.4 Chronic respiratory diseases 22 16.8 Chronic kidney diseases 17 12.9 Others 13 9..9 Note: DM- Diabetes Mellitus, DKA- Diabetes ketoacidosis, 3.3. Frequency DKA attack among adult DM patients Over the total 3-year follow-up period, 282 study participants (76.2%) developed DKA attacks, with more than three-quarters (75.2%) experienced recurrent (≥ 2) incidents. The mean number of DKA attacks was 2.2, with a maximum of 8 and a minimum of 0. More than one-fourth (21.8%) of the study participants experienced more than four DKA attacks. Regarding the severity of DKA attacks, the majority (169, or 59.9%) of DKA patients experienced mild attacks, while 14 (4.96%) experienced severe attacks. Both bivariable and multivariable zero-inflated negative binomial regression models were employed to identify predictors of the number of DKA attacks in DM patients (Fig. 1). 3.4 Predictors of DKA attack among adult DM patients In the final multivariable zero-inflated negative binomial regression models, the following variables, namely: residence, educational status, type of DM, health insurance enrollment status, and presence of comorbidities, were identified as significant predictors of recurrent DKA attacks among adult DM patients. Correspondingly, adult DM patients who reside in rural areas are 1.48 (AIRR = 1.48, 95% CI: 1.26–1.74) times more likely to develop an increased DKA attack than their counterparts. The incidence of DKA attacks among individuals who cannot read and write and in individuals with primary educational status is 1.52 (AIRR = 1.52, 95% CI: 1.13–2.04) and 1.55 (AIRR = 1.55, 95% CI: 1.1–2.05) times more likely when compared with individuals with tertiary and above educational status, respectively. In comparison to type II DM patients, the incidence of DKA is increased by 1.62 times in type I adult DM patients (AIRR = 1.62, 95% CI: 1.28–2.05). Moreover, the frequency of recurrent DKA attacks was 1.26 times (AIRR = 1.26, 95% CI: 1.10–1.48) more likely among individuals who were enrolled in health insurance compared to their counterparts. Finally, compared to their counterparts, those adult DM patients with comorbidity were 1.53 times (AIRR = 1.54, 95% CI: 1.29–1.81) at increased risk of developing DKA (Table 4 ). Table 4 Multivariable zero-inflated negative binomial regression analysis to identify predictors of frequency of DKA attack among adult DM patients in Debre Tabor Comprehensive Specialized Hospital, Northwest Ethiopia 2024 (N = 370) Variables Categories CIRR (95% CI) AIRR (95%CI) Age of the respondent 0.99 (0.98–0.99) 0.99 (0. 98 − 1.00) Sex of the respondent Male 1 1 Female 1.22 (1.05–1.41) 1. 05 (0.89–1.24) Residence Urban 1 1 Rural 1.58 (1.36–1.84) 1.44 (1.22 1.69) ** Educational status Cannot read and write 1.79 (1.40–2.29) 1.42 (1.06–1.91 ) * Primary education 1.74 (1.35–2.24) 1.47 (1.11–1.95) * Secondary education 1.18 (0.90–1.54) 0.96 (0.72 1.28) Tertiary education 1 1 Occupational status Employed 1 Not employed 1.71 (1.39–2.09) Health insurance membership status Enrolled 1 1 Not Enrolled 1.44 (1.24–1.68) 1.27 (1.07–1.49) Type of DM Type I DM 1.73 (1.46–2.02) 2.31 (1.40–3.79 ) ** Type II DM 1 1 Comorbidity Yes 1.15 (0.99–1.33) 1.50 (1.27–1.79) ** No 1 1 *- significantly associated with the incidence of DKA at a p-value < 0.05, **- significantly associated with the incidence of DKA at a P-value < 0.01, CI- Confidence Interval 4. Discussion This study primarily investigates the frequency of DKA attacks and their determinants among adult DM patients in DTCSH. It was observed that approximately 76.2% (95% CI: 71.9–80.0) of the study participants experienced at least one DKA attack during their follow-up period and more than three-fourth(75.2%) of the study participants had recurrent DKA attacks. This finding is consistent with previous studies conducted in Addis Ababa [ 31 ], at Adama Hospital Medical College in the Oromia region [ 32 ], at Jimma Medical Center in southwest Ethiopia [ 33 ] and at Hiwot Fana Comprehensive Specialized University Hospital (HFCSH) in Eastern Ethiopia[ 22 ]. On the other hand, the proportion of DKA attacks in this study is higher than in studies conducted in Hawassa University Comprehensive Specialized Hospital (HUCSH )in southern Ethiopia [ 34 ], Tanzania [ 35 ], Nigeria [ 27 ], Iraq [ 36 ], Italy[ 37 ] and Thailand[ 38 ]. In addition, the finding on recurrent DKA attack in this study is also supported by evidences from Jimma University Specialized Hospital, southwest Ethiopia[ 39 ] and HFCSH[ 22 ]. On the other hand, the above finding is higher than a study conducted in Qatar [ 40 ]. The possible elucidation for the observed discrepancy could be due to the presence of better health care structure, better access to medications and other health services, better awareness about diabetes management and better economic status in Qatar compared to Ethiopia which all contributes to the increased in the incidence of DKA attack. The largest Number of DKA attack in this study was eight, which is higher than a study conducted in King Fahad hospital, Madina, Saudi Arabia[ 41 ]. This could be due to the difference in economic and educational status of the communities in the two countries. Infection was the main precipitating factor of DKA, accounting for about 36.7%, followed by inadequate insulin therapy. This is because infections can lead to a variety of physiological responses in the body, including the release of stress hormones like cortisol and catecholamine’s, which can increase insulin resistance and promote the breakdown of fats, leading to ketone production. This is supported by studies conducted at Shashemene Referral Hospital in southern Ethiopia [ 13 ], Tanzania [ 11 ], Sub-Saharan African countries[ 12 ], King Abdulaziz University Hospital in Saudia Arabia[ 42 ], Pakistan[ 43 ] and China [ 44 ]. Furthermore, more than three-quarters (75.2%) of DKA patients presented with recurrent DKA incidents. This figure is higher than that reported in a study conducted at Grady Memorial Hospital in Atlanta [ 45 ]. The observed discrepancy might be explained by the fact that the healthcare system in the USA is highly developed compared to Ethiopia, resulting in patients having better access to seeking medical attention and obtaining optimal diabetes care[ 46 ]. In addition, lower levels of awareness and education about diabetes, coupled with the existence of economic inequalities; increase the rate of DKA in developing nations [ 8 , 47 , 48 ]. In the current study, we found that DM patients who reside in rural areas were 1.48 times more likely to have an increased incidence of DKA compared to their counterparts. This finding is supported by prior research conducted in Woldiya Comprehensive Specialized Hospital in northern Ethiopia [ 49 ],Hawassa [ 34 ],and Nigeria[ 50 ]. This might be explained by the fact that DM patients who reside in rural areas have limited access to healthcare[ 51 ], low health literacy levels[ 52 ], and awareness about diabetes mellitus management, as well as low socioeconomic status, which prevent individuals from seeking timely medical care or adhering to prescribed treatments. Additionally, they may have to travel long distances to reach the nearest healthcare facility[ 53 ]. On the contrary, research conducted at Basrah Teaching Hospital in Iraq[ 48 ] and in Sweden[ 54 ] reported that the incidence of DKA was not significantly different among rural and urban dwellers. The possible explanation for the observed discrepancies might be due to socioeconomic differences between those two countries and Ethiopia. The incidence of DKA attacks is 1.52 times higher among individuals who cannot read and write and 1.55 times higher among those with primary educational status compared to individuals with tertiary education or higher. This is in agreement with a study carried out in Basrah teaching hospital in Iraq [ 48 ] and Germany[ 55 ] The possible elucidation for the observed association might be due to the fact that individual’s with lower educational attainment may have poor understanding diabetes management, including proper medication adherence, dietary choices, and recognizing early signs of complications. Indeed, lower educational status might lead the individual to have limited income and poor living conditions, can contribute to unhealthy lifestyle behaviors and difficulty in accessing necessary medical care, further increasing the risk of DKA. Compared to individuals with type II diabetes mellitus, patients with type I diabetes mellitus were 1.62 times more likely to experience recurrent bouts of DKA attack. This finding was comparable to studies conducted in Debremarkos, northwest Ethiopia[ 17 ], a study conducted at North Wollo and Waghimra Zone public hospitals [ 56 ], Riyadh, Saudi Arabia [ 57 ] and at Soroka University Medical Center in Israel. This is because type I DM patients have absolute insulin deficiency, whereas T2DM patients are characterized by relative insulin deficiency, whereby the body has enough insulin to prevent lipolysis [ 58 – 60 ]. Insulin enhances the production of lipids while inhibiting lipolysis, the process of breaking down fats into fatty acids and glycerol. It also stimulates the absorption of fatty acids into adipose tissue for storage and diminishes their release into the bloodstream which lowers the blood lipid level and accumulations of fatty acids [ 61 ]. Additionally, individuals who did not enroll in health insurance have a 1.26 times higher risk of recurrent DKA attacks compared to their counterparts. This finding is supported by prior systematic review and meta-analysis research conducted on children and young adults [ 16 , 62 ], at the United States(US) tertiary academic medical center[ 63 ] and a study conducted at 16 hospitals in Guangdong province in China [ 64 ]. The increased risk of frequent DKA attacks among those without health insurance is attributed to the financial burden associated with ongoing management and treatment of chronic diseases, as well as limited access to healthcare services. Additionally, the health insurance may also cover the services including regular check-ups, monitoring and evaluation programs so that those who enrolled in such program could have effective DM management and prevent complications. Adult DM patients with comorbidities had a 1.53 times greater risk (AIRR = 1.54, 95% CI: 1.29–1.81) of developing recurrent episodes of DKA compared to their counterparts. This finding is consistent with studies conducted at Woldiya Comprehensive Specialized Hospital in northern Ethiopia [ 49 ], North Wollo and Waghimra zone public hospitals in Northern Ethiopia[ 56 ], Hawassa Comprehensive Specialized Hospital in southern Ethiopia [ 65 ], Jimma medical center in southwest Ethiopia [ 33 ], selected hospitals in Western Ethiopia [ 66 ] and Bugando Medical Centre in north-western Tanzania[ 11 ]. This might be best explained by the fact that comorbidities such as hypertension, cardiovascular disease, dyslipidemia, chronic obstructive pulmonary disease (COPD), asthma, and psychiatric disorders are linked with insulin resistance. This impairs the ability of cells to respond to insulin, leading to impaired glucose uptake. Consequently, the body resorts to breaking down fat for energy, resulting in the production of ketone bodies. Another possible mechanism could be medication non-adherence, dietary indiscretions, and inadequate monitoring of blood sugar levels secondary to the challenge in managing multiple chronic conditions. 5.1 Strength and Limitation of the study Though this study is the first of its kind to investigate the frequency of recurrent DKA attacks in Ethiopia, we recognize that it is subject to certain limitations. Firstly, since the data are collected by reviewing patients' medical records, it is difficult to examine the effect of some crucial variables including behavioral factors (such as smoking, alcohol consumption), biomedical-related factors (such as Body Mass Index, serum lipid profiles, hormone levels), perception, value, and belief-related factors, as well as the frequency of DKA. Secondly, recall and recording bias might be introduced due to the retrospective nature of some data, such as medication adherence. 5. Conclusion and recommendation The study noted that over half of Diabetes Mellitus (DM) patients experienced DKA, with more than two-thirds of them suffering from recurrent DKA episodes. Infection appeared as the primary trigger for recurrent DKA, followed by insufficient medication adherence. Factors such as place of residence, level of education, type of DM, enrollment status in health insurance, and the presence of other chronic disease comorbidities were pinpointed as significant predictors for recurrent DKA occurrences in adult DM patients. Stakeholders are advised to enhance community involvement in health insurance, improve the socio-economic status of the community, and provide due attention for type II DM patients and those with other comorbidities. Further qualitative and prospective studies should be undertaken to address important factors of recurrent DKA attacks. Abbreviations AIC- Akaike’s Information Criterion; AIRR- Adjusted Incidence Rate Ratio; BIC -Bayesian Information Criterion; CIRR- Crude Incidence Rate Ratio ; CI-confidence interval; DKA-Diabetic Ketoacidosis; DM- Diabetes Mellitus; DTCSH- Debre Tabor Comprehensive Specialized Hospital; FMOH- Federal Ministry of Health ; HUCSH- Hawassa University Comprehensive Specialized Hospital, HFCSH- Hiwot Fana Comprehensive Specialized University Hospital, IQR- Inter Quartile Range; SD-Standard Deviation; NCD- Non-Communicable Diseases; WHO, World Health Organization. Declarations Ethical Approval and Consent to participate An ethical approval letter was obtained from the Ethical Review Committee (ERC) of College of Health Science, Debre Tabor University. Due to the study’s retrospective nature, informed consent was waived by the ERC of college of health sciences with the protocol number Dtu/RP/206/16. Instead, a supportive letter was obtained from the hospital to ensure the use of patients' charts to obtain relevant data for the study. During data collection and entry, patient identifiers such as the patient's medical registration number (MRN) were replaced by new identification numbers or codes. Furthermore, all ethical methods were employed in accordance with relevant guidelines and regulations. Consent for Publication Not applicable. Data Availability All the data that has been used to draw the conclusion of this manuscript are available in the manuscript. Competing Interests The authors declare that they have no conflicts of interest for this work. Funds This study had no specific funding. Clinical Trial Number: not applicable. Author’s contribution BB, GKY, MUA, ABN, DH, TAB, TM, ME, YEA, ANM, and ASB participated in synthesizing the research question, formulating research objectives, data extraction, and preparing the initial draft of the manuscript. 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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. ELEDRISI M, ALABDULRAZZAK I, ALKABBANI HM, ABOAWON MK, ALI AA, ELHAJ MF, AHMED AOE, ALQAHWACHI H, MALIK RA: 1365-P: Recurrent Diabetic Ketoacidosis—Patient Characteristics and Clinical Outcomes in Qatar . Diabetes 2023, 72 (Supplement_1). Abulmagd M, Wdea EF, Salama AS, Samy BS, Zain AJ: AACE2021-A-1011: Recurrent Diabetic Ketoacidosis: Can We Prevent It? Endocrine Practice 2021, 27 (12):S17. Qari FA: Precipitating factors for diabetic ketoacidosis . Saudi medical journal 2002, 23 (2):173-176. Shahid W, Khan F, Makda A, Kumar V, Memon S, Rizwan A: Diabetic ketoacidosis: clinical characteristics and precipitating factors . Cureus 2020, 12 (10). Tan H, Zhou Y, Yu Y: Characteristics of diabetic ketoacidosis in Chinese adults and adolescents–a teaching hospital-based analysis . Diabetes research and clinical practice 2012, 97 (2):306-312. Randall L, Begovic J, Hudson M, Smiley D, Peng L, Pitre N, Umpierrez D, Umpierrez G: Recurrent diabetic ketoacidosis in inner-city minority patients: behavioral, socioeconomic, and psychosocial factors . Diabetes care 2011, 34 (9):1891-1896. Umpierrez GE, Kitabchi AE: Diabetic ketoacidosis: risk factors and management strategies . Treatments in endocrinology 2003, 2 :95-108. Große J, Hornstein H, Manuwald U, Kugler J, Glauche I, Rothe U: Incidence of diabetic ketoacidosis of new-onset type 1 diabetes in children and adolescents in different countries correlates with human development index (HDI): an updated systematic review, meta-analysis, and meta-regression . Hormone and Metabolic Research 2018, 50 (03):209-222. Al-Obaidi AH, Alidrisi HA, Mansour AA: Precipitating factors for diabetic ketoacidosis among patients with type 1 diabetes mellitus: the effect of socioeconomic status . International Journal of Diabetes and Metabolism 2019, 25 (1-2):52-60. 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. Amadi C, Ayoade OG, Essien SI, Etuk AA, Okafor CJ, Udoh EN, Umoh OO: The Incidence of Diabetic Ketoacidosis and Its Relationship with Residential Areas of Adults with Type 1 Diabetes in Nigeria . Tamirat KS, Tessema ZT, Kebede FB: Factors associated with the perceived barriers of health care access among reproductive-age women in Ethiopia: a secondary data analysis of 2016 Ethiopian demographic and health survey . BMC Health Services Research 2020, 20 (1):691. Aljassim N, Ostini R: Health literacy in rural and urban populations: A systematic review . Patient Education and Counseling 2020, 103 (10):2142-2154. Paiola E, Sartorello A, Andreani G, Tsegaye A, Tardivo S, Manenti F, Benoni R: Diabetic ketoacidosis among patients admitted to a general hospital in Ethiopia: a spatial analysis . European Journal of Public Health 2022, 32 (Supplement_3). Sadauskait≐-Kuehne V, Samuelsson U, Jašinskien≐ E, Padaiga Ž, Urbonait≐ B, Edenvall H, Ludvigsson J: Severity at onset of childhood type 1 diabetes in countries with high and low incidence of the condition . Diabetes Research and Clinical Practice 2002, 55 (3):247-254. Rosenbauer J, Icks A, Giani G: Clinical characteristics and predictors of severe ketoacidosis at onset of type 1 diabetes mellitus in children in a North Rhine-Westphalian region, Germany . Journal of Pediatric Endocrinology and Metabolism 2002, 15 (8):1137-1146. 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):26. Al-Hayek AA, Robert AA, Braham RB, Turki AS, Al-Sabaan FS: Frequency and associated risk factors of recurrent diabetic ketoacidosis among Saudi adolescents with type 1 diabetes mellitus . Saudi Medical Journal 2015, 36 (2):216. Hinkle JL: Brunner & Suddarth's Textbook of Medical-Surgical Nursing 14th Edition . In . : Wolters Kluwer; 2018. Loscalzo J, Fauci AS, Kasper DL, Hauser SL, Longo DL, Jameson JL: Harrison's principles of internal medicine . (No Title) 2022. Puttanna A, Padinjakara R: Diabetic ketoacidosis in type 2 diabetes mellitus . Practical Diabetes 2014, 31 (4):155-158. Rahman MS, Hossain KS, Das S, Kundu S, Adegoke EO, Rahman MA, Hannan MA, Uddin MJ, Pang M-G: Role of insulin in health and disease: an update . International journal of molecular sciences 2021, 22 (12):6403. Usher-Smith JA, Thompson MJ, Sharp SJ, Walter FM: Factors associated with the presence of diabetic ketoacidosis at diagnosis of diabetes in children and young adults: a systematic review . Bmj 2011, 343 . Bradford AL, Crider CC, Xu X, Naqvi SH: Predictors of recurrent hospital admission for patients presenting with diabetic ketoacidosis and hyperglycemic hyperosmolar state . Journal of clinical medicine research 2017, 9 (1):35. Yan J-h, Yang D-z, Deng H-r, Li J, Weng J-p: [Incidence and related risk factors of diabetic ketoacidosis in Guangdong type 1 diabetics] . Zhonghua Yi Xue Za Zhi 2013, 93 (12):897-901. Bedaso A, Oltaye Z, Geja E, Ayalew M: Diabetic ketoacidosis among adult patients with diabetes mellitus admitted to emergency unit of Hawassa university comprehensive specialized hospital . BMC research notes 2019, 12 :1-5. Korsa AT, Genemo ES, Bayisa HG, Dedefo MG: Diabetes mellitus complications and associated factors among adult diabetic patients in selected hospitals of West Ethiopia . The Open Cardiovascular Medicine Journal 2019, 13 (1). Additional Declarations No competing interests reported. Supplementary Files FrequencyofDKACopy.dta Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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DM patients in DTCSH, Northwest Ethiopia, 2024\u003c/p\u003e","description":"","filename":"FIGURE1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7316884/v1/ba238a29ae64ca543b2df21f.tif"},{"id":108547367,"identity":"f2a2ad2b-efe1-4083-9090-7fc9b64aca46","added_by":"auto","created_at":"2026-05-05 21:09:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":521856,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7316884/v1/e564c145-62e4-4c96-9998-d7295d385866.pdf"},{"id":91076724,"identity":"c9ae5468-b52f-4d85-aa3e-b521e095d630","added_by":"auto","created_at":"2025-09-11 11:12:14","extension":"dta","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":335935,"visible":true,"origin":"","legend":"","description":"","filename":"FrequencyofDKACopy.dta","url":"https://assets-eu.researchsquare.com/files/rs-7316884/v1/f0e3404439d9718ff4557a67.dta"}],"financialInterests":"No competing interests reported.","formattedTitle":"Frequency of Diabetes ketoacidosis (DKA) occurrence among adult Diabetes Mellitus patients in Ethiopia: A Negative Binomial Regression analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDiabetes ketoacidosis (DKA) is a life-threatening acute complication of diabetes mellitus (DM) characterized by the triad of hyperglycemic crisis, ketosis, and acidosis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although DKA has been reported to affect type 1 diabetes mellitus, patients with type 2 diabetes are also at risk during the catabolic stress of acute illnesses such as trauma, surgery, or infections[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It has been reported that DKA can be present in 25\u0026ndash;30% of type 1 diabetes cases at onset and from 4 to 29% in youth with type-2 diabetes[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In the last two decades, both the incidence and the frequency of DKA are rising [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In a setting where there is poor health system structure like Ethiopia, diabetes mellitus patients would have bouts of recurrent DKA attack ( greater than one episodes of DKA attack)[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The occurrence of recurrent diabetic ketoacidosis in diabetes mellitus patients highly depends on the socioeconomic conditions and the availability of medical services in the community. In this regard, omission of and/or inadequate insulin therapy due to transport problem or cost, presence of comorbidities, infection, alcohol/ other substance abuse, cerebrovascular accident, trauma and possible thyrotoxicosis were the main precipitating factors of DKA[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDKA in turn is the leading cause of mortality among DM patients, particularly in low income countries including Ethiopia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. With this aspect, the mortality rate in patients with diabetic ketoacidosis (DKA) is \u0026lt;\u0026thinsp;5% in experienced centers of America and Europe[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], whereas the mortality rate of patients in Africa is unexpectedly high, ranging from 6 to 30% [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] In Ethiopia, the mortality rate of DKA is high and highly varied across institutions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The high mortality rate in Africa may be partially attributed to poor adherence to diabetic medications and the insufficient availability of medication supplies. DKA is the main reason for the hospitalization of patients with DM, which may indirectly increases the health care cost of hospitalization and mortality rate of the patient[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Hospital admissions due to this problem constitute a serious economic burden on the healthcare system of particularly low- and -middle income countries. As the frequency of DKA attack is increasing, significant resources are spent on the cost of hospitalization. For instance, in United States of America (USA), DKA episodes represent\u0026thinsp;\u0026gt;\u0026thinsp;1 of every 4 USD spent on direct medical care for adult patients with type 1 diabetes and 1 of every 2 USD in patients experiencing multiple episodes[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Recurrent episodes of diabetic ketoacidosis (DKA) not only increase the risk of mortality but also significantly affect the quality of life for both individuals with diabetes and their families.\u003c/p\u003e\u003cp\u003eIn order to mitigate the detrimental effects of DKA, various strategies have been designed and implemented, including education on diabetes self-management options, improving the health service diabetes management system, and developing contextual DKA treatment guidelines [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In this regard, the Federal Ministry of Health (FMOH) of Ethiopia also developed a National Strategic Action plan (NSAP) for four priority Non-Communicable Diseases (NCD), including Diabetes mellitus[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Despite all the efforts made previously, the number of DKA episodes is increasing, becoming a major socioeconomic burden and health threat in Ethiopia. Additionally, although numerous studies have been undertaken in Ethiopia, the majority of them assess the proportion and treatment outcomes of DKA among diabetes mellitus patients[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Nevertheless, evidence on the frequency of DKA and its predictors among both type I and Type II diabetes mellitus patients is very limited. Assessing the number of DKA attacks and its factors in DM patients plays a paramount role in providing targeted interventions that would prevent further risks and associated healthcare costs and mortality rates.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design and Setting\u003c/h2\u003e\u003cp\u003eAn institutional-based retrospective follow-up study was conducted in northwest Ethiopia from September 2020 to June 2023. The study took place at Debre Tabor Comprehensive Specialized Hospital (DTCSH), situated in northwest Ethiopia. The hospital provides service to an estimated population of five million people residing in the surrounding areas.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study Participants\u003c/h2\u003e\u003cp\u003eAdult DM patients aged\u0026thinsp;\u0026gt;\u0026thinsp;15 years and who started a diabetic follow-up from September 2020 to June 2023 were included in the study. Adult DM patients with unknown dates of diagnosis were excluded from the study\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Sample size determination and sampling technique\u003c/h2\u003e\u003cp\u003eThe sample size for this study was determined using EPINFO version 7.2.3.1 software using parameters like 80% power, 95% confidence level, 10% incompleteness of charts, a 1 to 1 ratio of exposed to un-exposed individuals, and 39.3% and 24.5% incidence of DKA among exposed and non-exposed groups, respectively, in a study conducted at Bahirdar city referral hospitals [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Study participants were randomly selected from follow-up records (Sept 2020\u0026ndash;June 2023) using computer-generated sampling technique.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Study variable\u003c/h2\u003e\u003cp\u003eThe main outcome variable for this study is the number of DKA attacks per patient\u0026rsquo;s follow-up time. Predictor variables include socio-demographic factors (age, sex, educational status, occupation, residence, and family size), as well as clinical and treatment-related characteristics such as type and duration of diabetes mellitus, presence of comorbidities and infections, types of infection, and medication adherence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Operational definitions\u003c/h2\u003e\u003cp\u003eDiabetes ketoacidosis (DKA) is defined as the occurrence of a marked elevation of random blood glucose level (RBS\u0026thinsp;\u0026gt;\u0026thinsp;250 mg/dl), ketonuria (\u0026thinsp;\u0026ge;\u0026thinsp;+\u0026thinsp;2), and clinical features of DKA (frequent urination, thirst, abdominal pain, dehydration, and/or fruit odor breath) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The presence of comorbidities is defined as the occurrence of diabetes mellitus with other chronic medical illnesses. Incompleteness of charts was considered yes when variables like the diagnosis date of DM and age of the patient were missed in the medical record of patients\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Data Collection Procedures\u003c/h2\u003e\u003cp\u003eThe data abstraction tool was developed after reviewing various related literature and was pretested by randomly selecting 18 medical records. The tool encompasses socio-demographic, clinical, and therapeutic characteristics of individuals with DM. Subsequently, necessary data were extracted from patients' charts by three trained Bachelor of Science (BSC) nursing professionals. To ensure data quality, one-day training was provided to both the data collectors and supervisors on the study's purpose, how to extract relevant data, and how to maintain patient information confidentiality to ensure data quality. The principal investigator also monitored the data collection process.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Data processing and analysis\u003c/h2\u003e\u003cp\u003eThe data were entered into Epi_data version 4.6 and exported to STATA 14.2 software for analysis. descriptive statistics were applied, with continuous variables summarized using either mean and standard deviation (SD) or median and interquartile range (IQR), and frequency with percentage for categorical variables, respectively. Given that the outcome variable\u0026mdash;DKA episode frequency\u0026mdash;is count data, a Poisson regression model was employed to identify its predictors.\u003c/p\u003e\u003cp\u003eHowever, to run a Poisson regression model, the data should not be over dispersed, or the mean and variance should be equal. In the current data, the assumption is violated, and the data were over-dispersed (deviance/degree of freedom\u0026thinsp;=\u0026thinsp;1.489). To handle over-dispersed; we have considered another extension of Poisson regression models, namely zero-inflated Poisson (ZIP), negative binomial (NB), and zero-inflated negative binomial (ZINB) regression models. The deviance, Akaike\u0026rsquo;s Information Criterion (AIC), and Bayesian Information Criterion (BIC) values were used to select the best-fitting model \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e In this aspect, the zero-inflated negative binomial regression model was chosen as the best-fitting model. The omnibus test yielded p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating a statistically significant overall model. Variables with a p-value of \u0026lt;\u0026thinsp;0.25 in the bivariable analysis including age, sex, educational status, residence, being a member of health insurance clinical, Type of DM, presence of comorbidities, and presence of infections were fitted to the multivariable zero-inflated negative binomial regression model. The incidence rate ratio (IRR) with a 95% confidence interval was used to declare statistical significance. Variables with p-values less than 0.05 at the 95% confidence interval were considered a statistically significant factor affecting the number of DKA attacks among adult DM patients.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel comparison to select the best models to determine the predictors of frequency of DKA in Debre Tabor Comprehensive Specialized Hospital, 2024 (N\u0026thinsp;=\u0026thinsp;370)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS.no\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeviance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoisson regression model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,326.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1346.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1386.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZero-inflated Poisson regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1284. 16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1310 .16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1361.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative Binomial regression model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1321.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1343.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1386.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZero \u0026ndash;inflated negative binomial regression model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1284.16\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1308.14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1355.12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eNote: AIC \u0026ndash; Akaike\u0026rsquo;s Information Criterion, BIC- Bayesian Information Criterion\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8. Ethical considerations\u003c/h2\u003e\u003cp\u003e An ethical approval letter was obtained from the Ethical Review Committee (ERC) of College of Health Science, Debre Tabor University. Due to the study\u0026rsquo;s retrospective nature, informed consent was waived by the ERC of college of health sciences with the protocol number Dtu/RP/206/16. Instead, a supportive letter was obtained from the hospital to ensure the use of patients' charts to obtain relevant data for the study. During data collection and entry, patient identifiers such as the patient's medical registration number (MRN) were replaced by new identification numbers or codes. Furthermore, all ethical methods were employed in accordance with relevant guidelines and regulations.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1. \u003cb\u003eSocio-demographic characteristics of the study participants\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eA total of 370 (100%) study participants were included in the final analysis. Of the total, more than half (216 or 58.38%) were males. The mean age of the study participants was 40 with the standard deviation of \u0026plusmn;\u0026thinsp;14.14 years. Additionally, nearly half (183 or 49.46%) of the study participants live in rural areas \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline socio-demographic characteristics of study participants in Debre Tabor Comprehensive Specialized Hospital, 2024 (N\u0026thinsp;=\u0026thinsp;370)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026le;\u0026thinsp;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eMean age\u0026thinsp;=\u0026thinsp;40, SD (\u0026plusmn;\u0026thinsp;14.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eEducational\u003c/p\u003e\u003cp\u003estatus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot have formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertiary and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOccupation status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon- employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMember of health insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Baseline clinical characteristics of the study participants\u003c/h2\u003e\u003cp\u003eConsidering the clinical condition of the participants, more than half (208, or 56.2%) of them were type II DM patients. The median duration of DM for the study participants was 3 years, with an interquartile range (IQR) of 0 to 10 years. More than three-fourths (282, or 76.2%) of DM patients experienced at least one DKA event over the course of the 3-year follow-up period. The major precipitating factor for the incidence of DKA was infection, which accounts for about 36.7%, followed by inadequate anti-diabetes medication therapy. Additionally, more than one-third (131, or 35.5%) of the study participants had another chronic comorbidity. The mean plasma blood glucose level was 409.02\u0026thinsp;\u0026plusmn;\u0026thinsp;147.26 at the time of diagnosis and 244.15\u0026thinsp;\u0026plusmn;\u0026thinsp;100.10 at the end of follow-up \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline and follow-up clinical characteristics adult DM patients in Debre Tabor Comprehensive Specialized Hospital, Northwest Ethiopia 2024 (N\u0026thinsp;=\u0026thinsp;370)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eType of DM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eType I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eType II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDuration of DM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;5 years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMedian duration of DM\u0026thinsp;=\u0026thinsp;3 years (IQR\u0026thinsp;=\u0026thinsp;10\u0026thinsp;\u0026minus;\u0026thinsp;0 years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePlasma glucose level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eAt admission Mean\u0026thinsp;=\u0026thinsp;409.02 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;147.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eAt the end of follow-up Mean\u0026thinsp;=\u0026thinsp;244.15 ( SD\u0026thinsp;\u0026plusmn;\u0026thinsp;100.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDevelop DKA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003ePrecipitating factor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInfection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInadequate/missed diabetes medication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNewly diagnosed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePresence of comorbidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eType of comorbidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChronic heart diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChronic respiratory diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChronic kidney diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9..9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eNote: DM- Diabetes Mellitus, DKA- Diabetes ketoacidosis,\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Frequency DKA attack among adult DM patients\u003c/h2\u003e\u003cp\u003eOver the total 3-year follow-up period, 282 study participants (76.2%) developed DKA attacks, with more than three-quarters (75.2%) experienced recurrent (\u0026ge;\u0026thinsp;2) incidents. The mean number of DKA attacks was 2.2, with a maximum of 8 and a minimum of 0. More than one-fourth (21.8%) of the study participants experienced more than four DKA attacks. Regarding the severity of DKA attacks, the majority (169, or 59.9%) of DKA patients experienced mild attacks, while 14 (4.96%) experienced severe attacks. Both bivariable and multivariable zero-inflated negative binomial regression models were employed to identify predictors of the number of DKA attacks in DM patients \u003cb\u003e(Fig.\u0026nbsp;1).\u003c/b\u003e\u003c/p\u003e\u003ch2\u003e3.4 Predictors of DKA attack among adult DM patients\u003c/h2\u003e\n\u003cp\u003eIn the final multivariable zero-inflated negative binomial regression models, the following variables, namely: residence, educational status, type of DM, health insurance enrollment status, and presence of comorbidities, were identified as significant predictors of recurrent DKA attacks among adult DM patients. Correspondingly, adult DM patients who reside in rural areas are 1.48 (AIRR = 1.48, 95% CI: 1.26–1.74) times more likely to develop an increased DKA attack than their counterparts. The incidence of DKA attacks among individuals who cannot read and write and in individuals with primary educational status is 1.52 (AIRR = 1.52, 95% CI: 1.13–2.04) and 1.55 (AIRR = 1.55, 95% CI: 1.1–2.05) times more likely when compared with individuals with tertiary and above educational status, respectively. In comparison to type II DM patients, the incidence of DKA is increased by 1.62 times in type I adult DM patients (AIRR = 1.62, 95% CI: 1.28–2.05). Moreover, the frequency of recurrent DKA attacks was 1.26 times (AIRR = 1.26, 95% CI: 1.10–1.48) more likely among individuals who were enrolled in health insurance compared to their counterparts. Finally, compared to their counterparts, those adult DM patients with comorbidity were 1.53 times (AIRR = 1.54, 95% CI: 1.29–1.81) at increased risk of developing DKA (Table 4\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMultivariable zero-inflated negative binomial regression analysis to identify predictors of frequency of DKA attack among adult DM patients in Debre Tabor Comprehensive Specialized Hospital, Northwest Ethiopia 2024 (N = 370)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCIRR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAIRR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge of the respondent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0.98–0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0. 98 − 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSex of the respondent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22 (1.05–1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. 05 (0.89–1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58 (1.36–1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44 (1.22 1.69) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eEducational status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCannot read and write\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79 (1.40–2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42 (1.06–1.91\u003cstrong\u003e) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.74 (1.35–2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47 (1.11–1.95) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 (0.90–1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 (0.72 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTertiary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOccupational status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71 (1.39–2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHealth insurance membership status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnrolled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot Enrolled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44 (1.24–1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27 (1.07–1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eType of DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eType I DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.73 (1.46–2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31 (1.40–3.79\u003cstrong\u003e) **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eType II DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eComorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15 (0.99–1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50 (1.27–1.79) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e*- significantly associated with the incidence of DKA at a p-value \u0026lt; 0.05, **- significantly associated with the incidence of DKA at a P-value \u0026lt; 0.01, CI- Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study primarily investigates the frequency of DKA attacks and their determinants among adult DM patients in DTCSH. It was observed that approximately 76.2% (95% CI: 71.9\u0026ndash;80.0) of the study participants experienced at least one DKA attack during their follow-up period and more than three-fourth(75.2%) of the study participants had recurrent DKA attacks. This finding is consistent with previous studies conducted in Addis Ababa [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], at Adama Hospital Medical College in the Oromia region [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], at Jimma Medical Center in southwest Ethiopia [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and at Hiwot Fana Comprehensive Specialized University Hospital (HFCSH) in Eastern Ethiopia[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. On the other hand, the proportion of DKA attacks in this study is higher than in studies conducted in Hawassa University Comprehensive Specialized Hospital (HUCSH )in southern Ethiopia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], Tanzania [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], Nigeria [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], Iraq [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], Italy[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and Thailand[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In addition, the finding on recurrent DKA attack in this study is also supported by evidences from Jimma University Specialized Hospital, southwest Ethiopia[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and HFCSH[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. On the other hand, the above finding is higher than a study conducted in Qatar [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The possible elucidation for the observed discrepancy could be due to the presence of better health care structure, better access to medications and other health services, better awareness about diabetes management and better economic status in Qatar compared to Ethiopia which all contributes to the increased in the incidence of DKA attack. The largest Number of DKA attack in this study was eight, which is higher than a study conducted in King Fahad hospital, Madina, Saudi Arabia[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This could be due to the difference in economic and educational status of the communities in the two countries. Infection was the main precipitating factor of DKA, accounting for about 36.7%, followed by inadequate insulin therapy. This is because infections can lead to a variety of physiological responses in the body, including the release of stress hormones like cortisol and catecholamine\u0026rsquo;s, which can increase insulin resistance and promote the breakdown of fats, leading to ketone production. This is supported by studies conducted at Shashemene Referral Hospital in southern Ethiopia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], Tanzania [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], Sub-Saharan African countries[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], King Abdulaziz University Hospital in Saudia Arabia[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], Pakistan[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and China [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Furthermore, more than three-quarters (75.2%) of DKA patients presented with recurrent DKA incidents. This figure is higher than that reported in a study conducted at Grady Memorial Hospital in Atlanta [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The observed discrepancy might be explained by the fact that the healthcare system in the USA is highly developed compared to Ethiopia, resulting in patients having better access to seeking medical attention and obtaining optimal diabetes care[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In addition, lower levels of awareness and education about diabetes, coupled with the existence of economic inequalities; increase the rate of DKA in developing nations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the current study, we found that DM patients who reside in rural areas were 1.48 times more likely to have an increased incidence of DKA compared to their counterparts. This finding is supported by prior research conducted in Woldiya Comprehensive Specialized Hospital in northern Ethiopia [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e],Hawassa [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e],and Nigeria[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This might be explained by the fact that DM patients who reside in rural areas have limited access to healthcare[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], low health literacy levels[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and awareness about diabetes mellitus management, as well as low socioeconomic status, which prevent individuals from seeking timely medical care or adhering to prescribed treatments. Additionally, they may have to travel long distances to reach the nearest healthcare facility[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. On the contrary, research conducted at Basrah Teaching Hospital in Iraq[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and in Sweden[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] reported that the incidence of DKA was not significantly different among rural and urban dwellers. The possible explanation for the observed discrepancies might be due to socioeconomic differences between those two countries and Ethiopia.\u003c/p\u003e\u003cp\u003eThe incidence of DKA attacks is 1.52 times higher among individuals who cannot read and write and 1.55 times higher among those with primary educational status compared to individuals with tertiary education or higher. This is in agreement with a study carried out in Basrah teaching hospital in Iraq [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and Germany[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] The possible elucidation for the observed association might be due to the fact that individual\u0026rsquo;s with lower educational attainment may have poor understanding diabetes management, including proper medication adherence, dietary choices, and recognizing early signs of complications. Indeed, lower educational status might lead the individual to have limited income and poor living conditions, can contribute to unhealthy lifestyle behaviors and difficulty in accessing necessary medical care, further increasing the risk of DKA.\u003c/p\u003e\u003cp\u003eCompared to individuals with type II diabetes mellitus, patients with type I diabetes mellitus were 1.62 times more likely to experience recurrent bouts of DKA attack. This finding was comparable to studies conducted in Debremarkos, northwest Ethiopia[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], a study conducted at North Wollo and Waghimra Zone public hospitals [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], Riyadh, Saudi Arabia [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and at Soroka University Medical Center in Israel. This is because type I DM patients have absolute insulin deficiency, whereas T2DM patients are characterized by relative insulin deficiency, whereby the body has enough insulin to prevent lipolysis [\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Insulin enhances the production of lipids while inhibiting lipolysis, the process of breaking down fats into fatty acids and glycerol. It also stimulates the absorption of fatty acids into adipose tissue for storage and diminishes their release into the bloodstream which lowers the blood lipid level and accumulations of fatty acids [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, individuals who did not enroll in health insurance have a 1.26 times higher risk of recurrent DKA attacks compared to their counterparts. This finding is supported by prior systematic review and meta-analysis research conducted on children and young adults [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], at the United States(US) tertiary academic medical center[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] and a study conducted at 16 hospitals in Guangdong province in China [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. The increased risk of frequent DKA attacks among those without health insurance is attributed to the financial burden associated with ongoing management and treatment of chronic diseases, as well as limited access to healthcare services. Additionally, the health insurance may also cover the services including regular check-ups, monitoring and evaluation programs so that those who enrolled in such program could have effective DM management and prevent complications.\u003c/p\u003e\u003cp\u003eAdult DM patients with comorbidities had a 1.53 times greater risk (AIRR\u0026thinsp;=\u0026thinsp;1.54, 95% CI: 1.29\u0026ndash;1.81) of developing recurrent episodes of DKA compared to their counterparts. This finding is consistent with studies conducted at Woldiya Comprehensive Specialized Hospital in northern Ethiopia [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], North Wollo and Waghimra zone public hospitals in Northern Ethiopia[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], Hawassa Comprehensive Specialized Hospital in southern Ethiopia [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], Jimma medical center in southwest Ethiopia [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], selected hospitals in Western Ethiopia [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] and Bugando Medical Centre in north-western Tanzania[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This might be best explained by the fact that comorbidities such as hypertension, cardiovascular disease, dyslipidemia, chronic obstructive pulmonary disease (COPD), asthma, and psychiatric disorders are linked with insulin resistance. This impairs the ability of cells to respond to insulin, leading to impaired glucose uptake. Consequently, the body resorts to breaking down fat for energy, resulting in the production of ketone bodies. Another possible mechanism could be medication non-adherence, dietary indiscretions, and inadequate monitoring of blood sugar levels secondary to the challenge in managing multiple chronic conditions.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Strength and Limitation of the study\u003c/h2\u003e\u003cp\u003eThough this study is the first of its kind to investigate the frequency of recurrent DKA attacks in Ethiopia, we recognize that it is subject to certain limitations. Firstly, since the data are collected by reviewing patients' medical records, it is difficult to examine the effect of some crucial variables including behavioral factors (such as smoking, alcohol consumption), biomedical-related factors (such as Body Mass Index, serum lipid profiles, hormone levels), perception, value, and belief-related factors, as well as the frequency of DKA. Secondly, recall and recording bias might be introduced due to the retrospective nature of some data, such as medication adherence.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion and recommendation","content":"\u003cp\u003eThe study noted that over half of Diabetes Mellitus (DM) patients experienced DKA, with more than two-thirds of them suffering from recurrent DKA episodes. Infection appeared as the primary trigger for recurrent DKA, followed by insufficient medication adherence. Factors such as place of residence, level of education, type of DM, enrollment status in health insurance, and the presence of other chronic disease comorbidities were pinpointed as significant predictors for recurrent DKA occurrences in adult DM patients. Stakeholders are advised to enhance community involvement in health insurance, improve the socio-economic status of the community, and provide due attention for type II DM patients and those with other comorbidities. Further qualitative and prospective studies should be undertaken to address important factors of recurrent DKA attacks.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIC- Akaike\u0026rsquo;s Information Criterion; \u0026nbsp;AIRR- Adjusted Incidence Rate Ratio; BIC -Bayesian Information Criterion; CIRR- Crude Incidence Rate Ratio ; CI-confidence interval; DKA-Diabetic Ketoacidosis; DM- Diabetes Mellitus; DTCSH- Debre Tabor Comprehensive Specialized Hospital; FMOH- Federal Ministry of Health ; HUCSH- Hawassa University Comprehensive Specialized Hospital, HFCSH- Hiwot Fana Comprehensive Specialized University Hospital, IQR- Inter Quartile Range; \u0026nbsp; \u0026nbsp;SD-Standard Deviation; NCD- Non-Communicable Diseases; WHO, World Health Organization.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to participate \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn ethical approval letter was obtained from the Ethical Review Committee (ERC) of College of Health Science, Debre Tabor University. Due to the study\u0026rsquo;s retrospective nature, informed consent was waived by the ERC of college of health sciences with the protocol number Dtu/RP/206/16. Instead, a supportive letter was obtained from the hospital to ensure the use of patients\u0026apos; charts to obtain relevant data for the study. During data collection and entry, patient identifiers such as the patient\u0026apos;s medical registration number (MRN) were replaced by new identification numbers or codes. Furthermore, all ethical methods were employed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e Data Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data that has been used to draw the conclusion of this manuscript are available in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest for this work. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunds\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003eThis study had no specific funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e Clinical Trial Number: \u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBB, GKY, MUA, ABN, DH, TAB, TM, ME, YEA, ANM, and ASB participated in synthesizing the research question, formulating research objectives, data extraction, and preparing the initial draft of the manuscript. KGF, GBM, TD, ESC, CMT, YTK, MKH, and SFT were involved in data extraction, analysis, interpretation, conclusion, interpretation, and preparing the initial draft of the manuscript. All authors thoroughly read and approved the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKitabchi AE, Umpierrez GE, Miles JM, Fisher JN: \u003cstrong\u003eHyperglycemic crises in adult patients with diabetes\u003c/strong\u003e. \u003cem\u003eDiabetes care \u003c/em\u003e2009, \u003cstrong\u003e32\u003c/strong\u003e(7):1335.\u003c/li\u003e\n\u003cli\u003eChowdhury S, Dutta D: \u003cstrong\u003eGlycemic Emergencies: Diabetic Ketoacidosis, Hyperosmolar Nonketotic Hyperglycemia, and Hypoglycemia\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eNewton CA, Raskin P: \u003cstrong\u003eDiabetic ketoacidosis in type 1 and type 2 diabetes mellitus: clinical and biochemical differences\u003c/strong\u003e. \u003cem\u003eArchives of internal medicine \u003c/em\u003e2004, \u003cstrong\u003e164\u003c/strong\u003e(17):1925-1931.\u003c/li\u003e\n\u003cli\u003eAssociation AD: \u003cstrong\u003eHyperglycemic crises in diabetes\u003c/strong\u003e. \u003cem\u003eDiabetes Care \u003c/em\u003e2004, \u003cstrong\u003e27\u003c/strong\u003e(suppl_1):s94-s102.\u003c/li\u003e\n\u003cli\u003eDabelea D, Rewers A, Stafford JM, Standiford DA, Lawrence JM, Saydah S, Imperatore G, D\u0026rsquo;Agostino Jr RB, Mayer-Davis EJ, Pihoker C: \u003cstrong\u003eTrends in the prevalence of ketoacidosis at diabetes diagnosis: the SEARCH for diabetes in youth study\u003c/strong\u003e. \u003cem\u003ePediatrics \u003c/em\u003e2014, \u003cstrong\u003e133\u003c/strong\u003e(4):e938-e945.\u003c/li\u003e\n\u003cli\u003eDesai R, Singh S, Syed MH, Dave H, Hasnain M, Zahid D, Haider M, Jilani SMA, Mirza MA, Kiran N: \u003cstrong\u003eTemporal trends in the prevalence of diabetes decompensation (diabetic ketoacidosis and hyperosmolar hyperglycemic state) among adult patients hospitalized with diabetes mellitus: a nationwide analysis stratified by age, gender, and race\u003c/strong\u003e. \u003cem\u003eCureus \u003c/em\u003e2019, \u003cstrong\u003e11\u003c/strong\u003e(4).\u003c/li\u003e\n\u003cli\u003ePastakia SD, Pekny CR, Manyara SM, Fischer L: \u003cstrong\u003eDiabetes in sub-Saharan Africa\u0026ndash;from policy to practice to progress: targeting the existing gaps for future care for diabetes\u003c/strong\u003e. \u003cem\u003eDiabetes, metabolic syndrome and obesity: targets and therapy \u003c/em\u003e2017:247-263.\u003c/li\u003e\n\u003cli\u003eDel Degan S, Dub\u0026eacute; 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The frequency of DKA occurrences is an important indicator of both the adherence to management protocols and the quality of life of DM patients. However, information regarding the frequency and predictors of DKA among adult DM patients is limited in Ethiopia. Therefore, this study is aimed at investigating the frequency and associated factors of DKA among DM patients in northwest Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn institutional-based retrospective cohort study was conducted at Debre Tabor Comprehensive Specialized Hospital among 370 randomly selected patients. Data were extracted from patients' medical records, entered with Epidata 4.6 software, and exported to Stata 16.0 software for analysis. A zero-inflated negative binomial regression model was fitted to identify determinants of recurrent episodes of DKA. Adjusted incidence rate ratios (IRRs) with 95% confidence intervals were used to declare statistical significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003en the final analysis, all 370 (100%) study participants were included, with over half (208, or 56.2%) being type II DM patients. Throughout the three-year follow-up period, approximately 76.2% (95% CI: 71.9–80.0) of participants developed DKA, and among them, more than three-quarters (75.2%) experienced recurrent DKA attacks. Residing in a rural area (AIRR = 1.48, 95% CI: 1.26–1.74), being unable to read and write (AIRR = 1.52, 95% CI: 1.13–2.04), having primary-level educational status (AIRR = 1.55, 95% CI: 1.18–2.05), having type I DM (AIRR = 1.62, 95% CI: 1.28–2.05), not being enrolled in health insurance (AIRR = 1.26, 95% CI: 1.10–1.48), and having other comorbidities (AIRR = 1.54, 95% CI: 1.29–1.81) increase the frequency of DKA attacks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion and Recommendation:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, a high frequency of DKA was observed, with over three-quarters of DKA patients experiencing recurrent attacks. Rural residence, low educational attainment, type I DM, lack of health insurance enrollment, and comorbidities were identified as contributing factors. Stakeholders are urged to boost community engagement in health insurance, enhance socio-economic status, and prioritize type II DM patients and those with comorbidities.\u003c/p\u003e","manuscriptTitle":"Frequency of Diabetes ketoacidosis (DKA) occurrence among adult Diabetes Mellitus patients in Ethiopia: A Negative Binomial Regression analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 11:04:10","doi":"10.21203/rs.3.rs-7316884/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6b8e8a57-9044-425d-8051-564384364713","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T21:09:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 11:04:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7316884","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7316884","identity":"rs-7316884","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}