Covid-19 a Strong Predictor of Hyperglycaemia Among Ugandan Patients: A Retrospective Study

Covid-19 a Strong Predictor of Hyperglycaemia Among Ugandan Patients: A Retrospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Covid-19 a Strong Predictor of Hyperglycaemia Among Ugandan Patients: A Retrospective Study PAUL MUTOO BUKHOTA, MOSES KIRYA, DENIS BWAYO, JOHN PETER MASABA, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9012176/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: Hyperglycemia is one of the common complications in COVID-19 patients. Globally, hyperglycemia associated with COVID-19 was estimated to be 25%. Hyperglycemia results in increased morbidity and mortality yet proper screening and management protocols in developing countries. There is paucity of data in developing countries. Objectives: To determine the prevalence of hyperglycemia, clinical characteristics, and outcomes of COVID-19 admitted patients in Mbale and Soroti regional referral hospitals - Uganda. Methodology: Retrospective cross-sectional study was conducted on adult admitted COVID-19 patient’s case files at two tertiary hospitals, in Eastern Uganda. 711 COVID-19 patient files with a capillary blood glucose test result during the study period from 1st March 2020 to 31st December 2021 were reviewed. Data was abstracted into a data collection tool specifically designed for this study. The variables included socio-demographics, clinical characteristics, and outcome status of the patients. Hyperglycaemia was defined based on the COVID-19 management algorithm as capillary blood glucose readings >11.1 mmol/l at or during admission, with the aid of the Glucometer One-Touch©. A primary outcome was hyperglycaemia in hospitalised patients. The Chi-Squared test was used for bivariate analysis, while the logistic regression model was applied for multivariate analysis. Results: Overall, hyperglycaemia was detected in 459 out of 711 (65%) patients. Living in rural areas (AOR 1.7, 95% CI: 1.1-2.7, P < 0.027), having a medical history of diabetes mellitus (AOR 4.8, 95% CI: 3.4-6.7, P < 0.001), and current use of steroids (AOR 2.9, 95% CI: 1.8-4.7, P < 0.001) immediately before admission were statistically significantly associated with hyperglycemia in COVID-19 patients. COVID-19 found to be an independent risk factor for Hyperglycaemia. Conclusion: The prevalence of hyperglycaemia among COVID-19 patients in eastern Uganda during the global epidemic was high, at 65%. Pre-admission conditions associated with hyperglycaemia included a medical history of diabetes mellitus, steroid use, living in rural area. Strengthening screening for hyperglycemia and specific management protocols during epidemics and pandemics is recommended Prevalence Clinical characteristics Hyperglycaemia Diabetes Mellitus COVID-19 Eastern Uganda Figures Figure 1 Figure 2 Background Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus ( 1 – 4 ). COVID-19 was declared a pandemic by the World Health Organization (WHO) on March 11, 2020, in the Republic of China ( 5 ). Nearly 509,531,232 million COVID-19 cases had been confirmed globally by August 2023, with 770,437,327 million fatalities ( 6 ). According to the American Association of Diabetes and the American Association of Clinical Endocrinologists consensus, hospital-developed hyperglycaemia is defined as fasting capillary blood glucose ≥ 7.8 mmol/L in a patient with no history or evidence of diabetes, or random capillary blood glucose ≥ 11.1 mmol/L in diabetic patients. ( 7 ). Hyperglycaemia was commonly observed among patients admitted with COVID-19 who did not have a prior history of diabetes mellitus and were not using glucocorticoids. ( 8 ). Additionally, several studies found COVID-19 infection to be associated with the development of hyperglycaemia or new on-set type 2 diabetes mellitus (DM) ( 9 – 11 ). It has been observed that the prevalence of COVID-19-associated hyperglycaemia with or without pre-existing type 2 diabetes mellitus is 25%, while associated new-onset DM is 19% ( 12 ). Data further shows that both critically and non-critically ill COVID-19 patients present with higher than expected blood glucose levels, even in the absence of DM ( 13 ). Among COVID-19 patients, some studies have found hyperglycaemia to be an independent predictor of mortality and morbidity ( 14 ). The risk of all causes of death in COVID-19 patients with hyperglycaemia is nearly double compared to that of patients with pre-existing DM ( 15 ). Furthermore, hyperglycaemia has been associated with a prolonged hospital stay, often requiring life support with intensive care, mechanical ventilation, and renal replacement therapy ( 3 ). Additionally, poor quality of life and non-serious adverse events have also been reported ( 16 ). It has been postulated that apart from comorbidities associated with aging, such as hypertension, chronic lung disease, and diabetes, COVID-19 with hyperglycaemia could explain the poor outcomes among elderly COVID-19 patients ( 17 ). There are limited data on COVID-19-associated hyperglycaemia in Sub-Saharan Africa. The only study to assess the prevalence of hyperglycaemia in COVID-19 patients was conducted in South Africa, specifically KwaZulu-Natal, and found that 9.3% of admitted patients had hyperglycaemia ( 12 ). This study did not explore the association between different characteristics of COVID-19 patients and hyperglycaemia. This study aimed to investigate the prevalence of hyperglycaemia, associated factors among admitted COVID-19 patients in eastern Uganda. METHODS This was a retrospective cross sectional study conducted between 1st March 2020 and 31st December 2021. Study site The study was carried out on admitted adult COVID-19 patients at Mbale and Soroti regional referral hospital COVID-19 treatment centres. Mbale and Soroti Regional Referral Hospitals (RRHs) are the highest-tier public referral health facilities located in Eastern Uganda. These two hospitals are among the 13 regional referral hospitals in Uganda. Mbale Regional Referral Hospital (MRRH) is situated at the heart of Mbale City, while Soroti Referral Hospital (SRRH) is a government hospital located in Soroti city in eastern Uganda. More than 90% of the population live in rural communities. Mbale and Soroti regional referral hospitals were designated by the Ugandan Ministry of Health as COVID-19 treatment centres to admitted and manage COVID-19 patients in March 2020 when COVID-19 was declared a pandemic. Study population The study population included confirmed COVID-19 adult patients admitted in the catchment area of Mbale and Soroti regional referral hospital COVID-19 treatment centres during the period of the study. The Inclusion criteria included all confirmed COVID-19 cases with either positive reverse transcription polymerase chain reaction (RT-PCR) or positive RDT results, COVID-19 patients with documented test results for capillary Blood Sugar at two separate times during the hospital stay and 18 years and above including patients with complete admission files and records while exclusion criteria patients file with a single recording of blood sugar level or no results. All files of COVID-19 patients admitted to these hospitals from 1st March 2020 to 31st December 2021 were retrieved from their archives. A checklist with eligibility criteria was applied to each file. Sampling size and Sampling procedure The medical records with confirmed COVID-19 patients in the archives of Mbale and Soroti Regional Referral Hospital COVID-19 treatment centres were accessed. This was done by sampling of target files with COVID-19 patients above 18 years within the two treatment centres. We used capillary blood glucose (BG), because it is the most commonly used method in clinical practice for blood sugar level monitoring in our setting. All files of COVID-19 patients admitted to these hospitals from 1st March 2020 to 31st December 2021 were retrieved from their archives. A checklist with eligibility criteria was applied to each file. Files with either, COVID-19 PCR or RDT results and capillary blood glucose measurements on at least two separate occasions were selected for data abstraction. Subsequently, all files were returned to the hospital archives Infection control procedures such as social distancing, hand washing, and wearing face masks were observed during the data collection period. Data collection methods A data abstraction tool was developed to collect information from patient charts. The tool was adapted from the WHO-modified stepwise questionnaire( 4 ), adopted and contextualised to Ugandan context to collect data on demographics such as age, sex, tribe, education, employment, marital status, residence, weight, laboratory results, biophysical profile, COVID-19 status, and treatment status ( 18 ). Trained research assistants used the data collection tool to collect data. To obtain accurate and complete information from the COVID-19 patients' files, we trained four research assistants on how to use the selection criteria accurately, record data from the admission forms. The research teams were also trained in infection prevention and control measures for COVID-19, and they used facemasks, eye shields, and hand sanitizers during and after reviewing documents. The principal investigator supervised the data collection exercise from beginning to end, ensuring that every file was checked to confirm the accuracy of entries. The primary dependable variable was hyperglycaemia. It is a binary either a patient has hyperglycaemia or not recorded as YES for hyper and NO for no hyper Socio-demographic: Age, weight, height, Education level, Religion, Tribe, Marital status, Occupation, and clinical characteristics: Co-morbidities, type of drugs, clinical features, treatment, symptoms, laboratory results (capillary blood glucose) were the predictor variables. Trained research assistants extracted data on key variables of interest from confirmed COVID-19 patients' case files into the study questionnaire. The abstracted data included socio-demographic characteristics, clinical features, laboratory results (Capillary Blood Glucose), treatments administered, and clinical outcomes (death or discharge). The data was then entered into an electronic database using Google Forms. Completed data tools were reviewed daily by the principal investigator, and all completed tools were stored in a secure location. No names were used as identifiers on the data collection tools. Data capture screens with built-in checks for consistency, logical flow, range, and accuracy of data were designed in Epi-data version 5 and used for electronic data capture (data entry). Data entry was conducted at a secure office at the Busitema University College of Health Sciences, and all data were double-entered. All electronic data was saved on a password-protected external drive. All hard copies of the completed data collection tools were secured under lock and key and were only accessed by key data management team members. Epi data version 5 was used for data entry. We also used Stata 15 for cleaning and editing our data before data analysis. Once extracted into Excel, the data were sorted to identify exclusion criteria, notably incomplete data (mainly without clinical outcome status). Data processing The data were cleaned for errors and omissions at all stages of data processing. To ensure data quality, double-entry was performed to ensure correct data entry. Subsequently, the data were exported to Stata Version 15, where sorting, categorization, and necessary variable transformations were conducted. Data analysis: The data were analyzed using Stata (version 15.0, College Station, Texas 77845 USA). Proportions and percentages were determined for categorical variables at the univariate and bivariate levels of analysis. The prevalence of hyperglycaemia in COVID-19 patients was determined and reported as a percentage with 95% confidence intervals. Univariate analysis was used to summarize the clinical and demographic characteristics of our study population. For bivariate analysis, differences between continuous variables were determined using Student’s t-test for normally distributed variables, while differences between categorical variables were determined using the Chi-square test as appropriate. The 95% confidence interval (CI) was set, with p < 0.05 considered statistically significant. Associations between the dependent variable (hyperglycemia) and independent variables (sociodemographic and clinical characteristics) were initially assessed by bivariate analysis. Multivariate regression models were fitted for all clinical and demographic variables to control for confounding and identify prognostic factors, using death as the outcome variable with a significance level of p = 0.05. In the multivariate analyses, the independent variables were also checked for interaction with a significance level of p < 0.1. Significant results from the bivariate analysis were used in the multivariate analysis to determine their independent effects on the dependent variables. Results were interpreted using adjusted odds ratios (aORs) and 95% CI. To describe the outcome, a risk analysis was performed using multivariate regression analysis with death as the outcome variable and clinical and demographic characteristics, including hyperglycaemia, as the independent variables. RESULTS Out of 1865 files retrieved from the archive of extracted from both Mbale and Soroti Regional Referral Hospital COVID-19 treatment centres, we extracted retrieved 766 patients’ files and obtain full records for 711 patients’ files were included in this study. The median age of participants was 58 years, interquartile range (IQR) 18 to 98 years. The median age associated with onset of hyperglycemia 57 and interquartile range was 18 to 98. Table 1 Socio-demographic characteristics of patients with COVID-19 with hyperglycaemia Variable Total n = 711(%) Hyperglycaemia COR(95% CI) P-value No n = 252(%) Yes n = 459(%) Sex 0.878 Female 333(46.8) 119(47.2) 214(46.6) 1 Male 378(53.2) 133(52.8) 245(53.4) 1.02(0.8, 1.4) 0.878 Age (Years) 0.001 18–29 44(6.2) 6(2.4) 38(8.3) 1 30–39 69(9.7) 11(4.4) 58(12.6) 0.8(0.3, 2.4) 0.738 40–49 118(16.6) 52(20.6) 66(14.4) 0.2(0.1, 0.5) 0.001 50–59 152(21.4) 58(23.0) 94(20.5) 0.3(0.1, 0.6) 0.004 60+ 328(46.1) 125(49.6) 203(44.2) 0.3(0.1, 0.6) 0.003 Level of education completed 0.001 No formal schooling 170(23.9) 13(5.2) 157(34.2) 1 Primary school 277(39.0) 137(54.4) 140(30.5) 0.1(0.04, 0.2) 0.001 Secondary school 116(16.3) 51(20.2) 65(14.2) 0.1(0.1, 0.2) 0.001 Tertiary level 148(20.8) 51(20.2) 97(21.1) 0.2(0.1, 0.3) 0.001 Employment 0.001 Business 94(13.2) 34(13.5) 60(13.1) 1.1(0.7,1.9) 0.689 Formal employment 155(21.8) 60(23.8) 95(20.7) 1 Peasant 360(50.6) 140(55.6) 220(47.9) 1.0(0.7, 1.5) 0.969 Student 21(3.0) 1(0.4) 20(4.4) 12.6(1.7, 96.6) 0.015 Unemployed 81(11.4) 17(6.7) 64(13.9) 2.4(1.3, 4.4) 0.007 Residence 0.002 Rural 527(74.1) 204(81.0) 323(70.4) 1 Urban 184(25.9) 48(19.0) 136(29.6) 1.8(1.2, 2.6) 0.002 A known DM 497(69.9) 102(40.5) 395(86.1) 9.1(6.3, 13.1) 0.001 Socio-demographic characteristics of COVID-19 patients with hyperglycaemia As shown in Table 1 there were more males, 53.2% (378/711) and majority 46.1% (328/711) of the patients were aged over 60 years. The majority lived in rural 74.1% (527/711) and were peasants 50.6% (360/711). Hyperglycaemia increased with age. The proportion of patients with hyperglycaemia was highest in those more than 60 years with 44.2% (203/459) The proportion of patients with hyperglycaemia was highest in those people who had no education at 34.2% (157/459) and lowest in those who had the highest level of secondary education at 14.2% (65/459). More peasants were more likely to have hyperglycaemia with a proportion of 47.9% (220/459) while few students presented with hyperglycaemia with a proportion of 4.4% (20/459). There was also a statistically significant association between hyperglycaemia and place of residence. There was a high proportion of patients from rural areas presenting with hyperglycaemia, 70.4% (323/459) compared to those from urban areas 29.6% (136/459) who were in the rural areas were more likely to present with hyperglycaemia compared to those in Urban area. Prevalence of hyperglycaemia among COVID-19 patients The overall prevalence of hyperglycaemia among COVID-19 patients was found at (65%: 95%CI 0.6–0.7) (459/771) as shown in Fig. 1 below. However, among those with no history of known DM, the hyperglycaemia prevalence was 46.2% (153/331) while that with known DM it was at 80.5% (306/380) on admission as illustrated in Fig. 2 below. Figure 1: Prevalence of hyperglycaemia among COVID-19 patients Clinical characteristics of COVID-19 patients with hyperglycaemia Bivariate analysis of clinical characteristics, considering the independent variable and hyperglycaemia using logistic regression, found that having a medical history of diabetes mellitus (COR = 4.8, 95% CI: 3.4–6.7, p < 0.001), hypertension (COR = 2.3, 95% CI: 1.6–3.3, p < 0.001), current use of steroids (COR = 2.9, 95% CI: 1.8–4.7, p < 0.001), and dextrose infusion (COR = 1.8, 95% CI: 1.1–2.9, p = 0.023) before admission were significantly associated with the development of hyperglycemia among COVID-19 patients at a 95% confidence interval (Table 2 ). Furthermore, presenting with clinical features such as feeling tired or fatigued or weakness (COR = 2.6, 95% CI: 1.5–4.5, p < 0.001), weight loss (COR = 0.4, 95% CI: 0.3–0.7, p < 0.001), headache or blurred vision (COR = 0.3, 95% CI: 0.2–0.5, p < 0.001), fever (COR = 1.8, 95% CI: 1.1–2.9, p = 0.020), abdominal pain (COR = 8.5, 95% CI: 1.1–64.6, p = 0.014), and coma (COR = 2.8, 95% CI: 1.8–4.4, p < 0.001) were also statistically significantly associated with hyperglycemia among COVID-19 patients. Table 2 Clinical characteristics of COVID-19 patients that developed hyperglycaemia Variable 1 Total n = 711 Hyperglycaemia COR(95% CI) P-value No n = 252(%) Yes n = 459(%) History of Diabetes Mellitus(n = 658)* 380(57.8) 74(29.4) 306(66.7) 4.8(3.4, 6.7) 0.001 Hypertension 216(30.4) 50(19.8) 166(36.2) 2.3(1.6, 3.3) 0.001 chronic heart disease 24(3.4) 5(2.0) 19(4.1) 2.1(0.7, 5.8) 0.137 cerebrovascular disease 13(1.8) 1(0.4) 12(2.6) 6.7(0.9, 52.1) 0.068 chronic lung disease 8(1.1) 2(0.8) 6(1.3) 1.7(0.3, 8.3) 0.539 chronic liver disease( n = 704)* 8(1.1) 2(0.8) 6(1.3) - 0.117 Chronic kidney disease 8(1.1) 1(0.4) 7(1.5) 3.9(0.5, 31.8) 0.205 Current treatment before Admission Steroids(n = 543) * 132(18.6) 24(9.5) 108(23.5) 2.9(1.8, 4.7) 0.001 Antipsychotics(n = 544) * 8(1.1) 1(0.4) 7(1.5) 3.9(0.5, 31.8) 0.205 Dextrose infusions(n = 538) * 96(13.5) 24(9.5) 72(15.7) 1.8(1.1, 2.9) 0.023 Thiazide diuretics(n = 540) * 11(1.5) 5(2.0) 6(1.3) 0.7(0.2, 2.2) 0.487 Parenteral/Enteral nutrition(n = 545) * 16(2.3) 3(1.2) 13(2.8) 2.4(0.7, 8.6) 0.171 Anti-retroviral therapy(n = 541) * 6(0.8) 0(0.0) 6(1.3) - 0.068 Current Treatment for DM during Admission Insulin 292(41.1) 94(37.3) 198(43.1) 1.3(0.9, 1.7) 0.131 Oral anti-hyperglycemic agents 91(12.8) 24(9.5) 67(14.6) 1.6(0.9, 2.7) 0.054 Symptoms (n = 704) * Frequent urination 23(3.2) 6(2.4) 17(3.7) 1.6(0.6, 4.1) 0.340 Increased thirst(polydipsia) 10(1.4) 4(1.6) 6(1.3) 0.8(0.2, 2.9) 0.762 Increased appetite (polyphagia) 417(58.6) 198(78.6) 219(47.7) 0.2(0.2, 0.4) 0.001 Feeling tired or fatigued or weakness(n = 702) * Tired 16(6.3) 68(14.8) 2.6(1.5, 4.5) 0.001 weight loss(n = 702) * 512(72.0) 205(81.3) 307(66.9) 0.4(0.3, 0.7) 0.001 Headache or/ Blurred vision(n = 701) * 496(69.8) 210(83.3) 286(62.3) 0.3(0.2, 0.5) 0.001 Fever(n = 705) * 100(14.1) 25(9.9) 75(16.3) 1.8(0.1, 2.9) 0.020 Abdominal pain(n = 699) * 16(2.3) 1(0.4) 15(3.3) 8.5(1.1, 64.6) 0.014 Coma(n = 699) * 142(20.0) 27(10.7) 115(25.1) 2.8(1.8, 4.4) 0.001 Shortness of breath(n = 704) * 26(3.7) 0(0.0) 26(5.7) - 0.001 *Some data is missing. 1 Multiple response is possible DISCUSSION The study found the prevalence of hyperglycaemia among COVID-19 patients to be 459/711(65%). This is closely similar to other reported studies. A retrospective observational study by viet et al 2022 among 517 COVID-19 adults with hyperglycemia in severe and critical COVID-19 patients in field hospital showed a prevalence of 65.6%( 19 ). This could be attributed to several factors including the study design since we used a retrospective cross sectional study basing on hospital records. Further cross sectional observational study by Ad’hiah et al 2021 among 213 COVID-19 patients reported 22.5% prediabetes and 52.1% diabetes in patients without prior history of diabetes( 20 ) It has been reported by Mohan et al (2021) in Korea among 7341 patients with COVID-19 can lead to glucose dysregulation and induce hyperglycaemia through various mechanisms like inflammation, stress, and direct effects on pancreatic cells ( 21 ). Furthermore, our study also examined the prevalence of hyperglycaemia among COVID-19 patients based on their diabetic status. Among unknown non-DM COVID-19 patients, the prevalence of hyperglycaemia was found to be 46.2%. In contrast, known DM COVID-19 patients had a higher prevalence of hyperglycaemia at 80.5%. These findings align with previous research highlighting that individuals with pre-existing diabetes are more likely to experience hyperglycaemia. A cross sectional study among 7,337 patients with COVID-19 in Hubei Province, China in 2020 by She, Zhu ( 22 ) showed that the interaction between COVID-19 and diabetes can worsen glycemic control, leading to increased complications and 7.8% mortality. Another study by Zhu et al. (2020) conducted in China reported similar findings, indicating that hyperglycaemia was more prevalent in diabetic COVID-19 patients compared to non-diabetic patients with an unknown diabetic status. ( 22 , 23 ). Additionally, a 46.2% prevalence of hyperglycaemia in previously unknown non-diabetic patients is unexpectedly high. This finding highlights the increased need for monitoring COVID-19 patients, regardless of their medical history of diabetes. The occurrence of hyperglycaemia in non-diabetic COVID-19 patients could be attributed to the reported phenomenon of stress-induced hyperglycaemia, where the body responds to trauma, infections, or sepsis by increasing blood sugar levels through the activation of various neuroendocrine pathways ( 7 ). According to our findings, approximately 53.2% of the patients included in the study were male, indicating a slightly higher representation of males in the study population. This is also similar to a retrospective single centre study by Mazori et al. (2021) conducted among 133 critically ill COVID-19 patients at an Urban academic quaternary care centre, which found that 69% were male patients ( 24 ). Another study from Turkey by Tahir Belice et al. revealed that diabetic men had a higher risk of mortality (men 40.9% and women 18.2%), and the rates of admission were higher for men with COVID-19 compared to women (women 17.1% versus men 47.4%) than for other diseases ( 25 ).. The reason for this gender distribution could be attributed to various factors, such as differences in exposure to risk factors, or variations in susceptibility to COVID-19 between males and females. We found that majority, 46.1% of the patients, were over 60 years old. Older age has been consistently identified as a risk factor for severe COVID-19 outcomes in numerous studies worldwide. A retrospective cohort study conducted by Zhou et al. (2020) in Jinyintan and Wuhan pulmonary hospital China on 191 COVID-19 patients reported that advanced age was associated with higher mortality rates 36% among COVID-19 patient ( 23 ). This is also similar to a study by Mazori et al. (2021) conducted among critically ill COVID-19 patients in Malaysia, which found that older age was significantly associated with hyperglycaemia in COVID-19 patients. The prevalence of hyperglycaemia was much higher among older patients 46.6% compared to younger ones ( 24 ). Physiological changes associated with aging, such as decreased insulin sensitivity and impaired glucose regulation, contribute to higher glucose levels. Therefore, the higher prevalence of older individuals in the study could reflect the increased vulnerability of this age group to severe COVID-19 disease. The majority of the patients lived in rural areas 70.4% and were peasants. Similar findings have been reported in other studies. A systematic review by Mehraeen et al. (2020) showed a higher pooled prevalence of 69.8% of hyperglycaemia among COVID-19 patients living in rural areas compared to those living in urban areas. These findings suggest a higher representation of individuals from rural settings and an occupation primarily associated with agricultural work. Rural populations may face unique challenges in terms of access to healthcare facilities, resources, and information. Limited access to quality healthcare in rural areas could potentially impact the detection, management, and outcomes of hyperglycaemia associated with COVID-19. This is in agreement with Mehraeen et al., 2020 studies suggested that limited access to healthcare resources and disparities in healthcare services in rural areas might have contributed to this association ( 26 ). The study identified at the following characteristics; history of DM, hypertension, and cerebrovascular disease. It also reported high prevalence rates of hyperglycaemia in COVID-19 patients with specific chronic illnesses, such as chronic kidney disease. Similar findings were reported by Singh et al. (2020) in a retrospective study conducted in India among 1093 admitted COVID-19 patients in a referral hospital, which found that a history of DM and hypertension were independent risk factors for the development of hyperglycaemia in COVID-19 patients ( 27 ). Additionally, the current use of steroids before admission was also associated with the development of hyperglycaemia. This could be due to the effects of steroids on the body's glucose metabolism; steroids have been reported to increase the body's mobilization of blood sugar levels. These findings are consistent with other studies conducted in different populations. Similarly, a systematic review by Jung et al. (2021) in Pakistan, where they conducted aggregate data meta-analyses, trial sequential analyses, network meta-analyses, and individual patient data meta-analyses on 81 clinical trials, identified steroid use as a significant predictor of hyperglycaemia in COVID-19 patients ( 28 ). Dextrose contains glucose, so its use directly raises blood sugars. This implies that cautious use of steroids and dextrose in COVID-19 patients is required for good clinical outcomes. Furthermore, the use of certain other medications, including antiretroviral therapy, antipsychotics, and parenteral nutrition, was associated with a higher prevalence of hyperglycaemia, although the prevalence was relatively lower in patients treated with insulin compared to those treated with oral hypoglycemic agents. Presenting clinical features such as feeling tired or fatigued, weakness, weight loss, headache or blurred vision, fever, abdominal pain, coma, shortness of breath, and dehydration were significant. Similar findings have been reported in other studies. For instance, a study by Sachdeva et al. (2020) conducted in India found an association between fatigue, weight loss, and hyperglycaemia in COVID-19 patients. The study also reported an increased prevalence of hyperglycaemia in patients with fever and respiratory symptoms ( 29 ). Additionally, a retrospective study by Wu et al. (2020) in China found that the elevation of blood glucose levels predicts worse outcomes in hospitalized patients with COVID-19. They identified symptoms such as fatigue, dyspnea, and dehydration as indicators of severe COVID-19 and potential risk factors for hyperglycaemia ( 30 ). These clinical symptoms are nonspecific, as they can also occur in other disease conditions. This implies that the detection of hyperglycaemia among COVID-19 patients is not possible solely by clinical symptoms, but rather requires blood sugar level monitoring The study reported high prevalence rates of hyperglycaemia in COVID-19 patients with specific chronic illnesses, such as cerebrovascular disease, and chronic kidney disease. Furthermore, the use of certain medications, including antiretroviral therapy, antipsychotics, steroids, and parenteral nutrition, was associated with a higher prevalence of hyperglycaemia. However, we could not explain why patients on antipsychotics, and parenteral nutrition were associated with hyperglycaemia. The prevalence was relatively lower in patients treated with insulin compared to those treated with oral hypoglycemic agents. This difference could be explained by the fact that insulin is more potent than oral hypoglycemic agents in controlling blood sugar. The study found that living in rural areas was associated with a higher risk of developing hyperglycaemia in COVID-19 patients. These findings are consistent with those of Sharma et al. (2021) in the United Kingdom, who demonstrated that urban residency was associated with an increased risk of developing hyperglycaemia among COVID-19 patients ( 31 ). One possible explanation for this higher risk could be that urban areas often have different lifestyles and dietary habits compared to rural areas. Urban populations may have a higher consumption of processed foods, junk food, sugary beverages, and sedentary lifestyles, which are known risk factors for hyperglycaemia and diabetes. These unhealthy habits may exacerbate the impact of COVID-19 on glucose regulation and increase the likelihood of hyperglycaemia in infected patients. A study by Mehraeen et al. (2020) conducted in Iran found that living in rural areas was associated with a lower risk of hyperglycaemia in COVID-19 patients ( 26 ). The COVID-19 patients who had a medical history of diabetes mellitus had 4.8 times increased odds of developing hyperglycaemia. The presence of diabetes mellitus can impair glucose regulation, and when coupled with COVID-19 disease, which is a stressor to the body, individuals are more susceptible to hyperglycaemia. These findings align with the existing body of evidence, emphasizing the importance of closely monitoring and managing blood glucose levels in COVID-19 patients with a history of diabetes mellitus. Numerous studies have reported that pre-existing diabetes is a significant risk factor for hyperglycaemia in individuals with COVID-19 ( 9 , 32 , 33 ). The current use of steroids before admission was associated with 2.9 times higher odds of developing hyperglycaemia in COVID-19 patients. Steroid medications, such as dexamethasone, have been widely used in the treatment of severe COVID-19 cases to reduce inflammation and improve outcomes. However, steroids can also lead to hyperglycaemia by increasing the catabolism of different body metabolites, thereby increasing blood glucose levels. Several studies have reported that steroids, particularly high-dose glucocorticoids, can induce or exacerbate hyperglycaemia by increasing insulin resistance and impairing glucose tolerance ( 27 , 34 ). These findings have been observed in studies conducted in various populations and settings, including both hospital and community settings. The study also found that COVID-19 patients who were aged 40 years and older and had formal education were associated with a 0.2 lower risk of developing hyperglycaemia. Older age (40 years and older) and formal education may reflect better overall health literacy and a higher likelihood of practicing healthy lifestyle behaviours, which could contribute to better glucose regulation. Study strengths The strengths of this study, it’s the first documented study that assessed hyperglycemia among COVID-19 patients in two public hospitals both urban and rural settings in eastern Uganda thus our results are representative of a bigger population of hospitalized COVID-19 patients in the country. The sample size was big enough and powered enough to identify potential associations between the variables of interest. This study looked at capillary blood glucose levels to arrive at the overall prevalence outside ICU settings, compared to several studies that looked at ICU patients. The research provides potential information in the development of clinical protocol on routine screening for hyperglycemia during epidemics or even pandemics to cater for routine screening and as well as timely assessment and treatment of hyperglycemia and its complications in COVID-19 patients. Study limitations The retrospective nature of the study, obtaining all the data in relation to parameters of interest was not possible. So the results might have been subject to under reporting There was missing information in some patient’s files which is a challenge especially the patients who were transferred out of the participating hospitals. CONCLUSIONS The prevalence of hyperglycaemia among hospitalised patients was high in our study. Factors associated with development of hyperglycaemia included increasing age, living in rural areas and male gender while clinical characteristics were history of diabetes mellitus and current use of steroids. Hyperglycaemia was found to be an independent risk factor among COVID-19 patients. We therefore recommend that; standardised guidelines for screening and treating hyperglycaemia in all COVID-19 patients and should be incorporated into the routine care package during epidemics and pandemics. Abbreviations ACE2 Angiotensin-converting enzyme 2 CBD Capillary Blood Glucose CI Confidence Interval COVID-19 SARS-cov-2 CRP C-reactive protein CTU COVID-19 Treatment Unit DKA Diabetic Ketoacidosis DM Diabetes Mellitus DNA deoxyribonucleic acid FPG Fasting Plasma Glucose HDL High-Density Lipoprotein HDU High dependency unit HHS Hyperosmolar Hyperglycemic State ICU Intensive Care Unit LDL Low-Density Lipoprotein mmHg millimeter of mercury mmol/l millimole per litre MOH Ministry of Health MRRH Mbale Regional Referral Hospital OR Odds Ratio PCR Polymerase chain reaction RBS Random Blood Sugar RNA Ribonucleic Acid RR Relative Risk SD Standard Deviation SOPs Standard Operating Procedures SRRH Soroti Regional Referral Hospital SSA Sub-Saharan Africa T2D Type 2 diabetes mellitus Tc Total cholesterol TG Triglyceride UNCST Uganda National Council of Science and Technology WHO World Health Organization Declarations Acknowledgments We would like to acknowledge the management of Mbale and Soroti Regional Hospital for permitting us to conduct our study and for their support throughout the data collection period. We are specifically thankful to COVID-91 treatment centres records department who helped us with the data collection. Authors’ contributions PMB contributed to the design, analysis, and article write-up. PMB was the principal investigator and conceptualized the overall design, conducted the study, and article write-up. POO contributed to the conceptualization, and overall design, and supervised the research process, analysis, and article write-up. RK supervised the research process at all stages, including analysis and interpretation and article write-up. DB contributed to the restructuring of the paper, and writing up of the article, consented to the publication, and guided the overall design of intervention and article write-up. JPM contributed to the design, and article write-up, and ensured adherence to the research protocols in the implementation, including overall coordination. OA contributed to data entry, data analysis, and interpretation. Funding statement; Research reported in this publication was supported by the Fogarty International Centre of the National Institutes of Health, U.S. Department of State’s Office of the U.S. Global AIDS Coordinator and Health Diplomacy (S/GAC), and President’s Emergency Plan for AIDS Relief (PEPFAR) under Award Number 1R25TW011213. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Availability of data and materials The datasets used during our study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The Data was collected by reviewing documents with no direct interaction with the patients (patient’s files). The study was approved by the Busitema University Research and Ethics Committee (REC), No. PDF 2022-11-03). We also got administrative clearance from Mbale and Soroti Regional Referral Hospital REC to access the patient's files. All data and study documents were stored securely, according to Good Clinical Practice 10 on secure storage of research materials. We sought waiver of consent from patients the Busitema University Research and Ethics committee (REC) and was granted, since we were reviewing patient’s records. Throughout the study, we did not use any unique patient identifiers and we complied with the Helsinki Declaration Consent for publication Not applicable Competing interests The authors declare no competing interests. Author details Internal Medicine at the Faculty of Health Sciences, Busitema University, Mbale, Uganda References WHO. Coronavirus disease (COVID-19) 2021 [cited 2023 28TH/07/2023]. Available from: https://www.who.int/health-topics/coronavirus#tab=tab_1. Synowiec A, Szczepański A, Barreto-Duran E, Lie LK, Pyrc K. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): a Systemic Infection. Clinical microbiology reviews. 2021;34(2). Mirzaei F, Khodadadi I, Vafaei SA, Abbasi-Oshaghi E, Tayebinia H, Farahani F. Importance of hyperglycemia in COVID-19 intensive-care patients: Mechanism and treatment strategy. Prim Care Diabetes. 2021;15(3):409-16. ORGANISATION WH. WHO STEPS Surveillance Manual 2 October 2020 Cucinotta D, Vanelli M. WHO Declares COVID-19 a Pandemic. Acta bio-medica : Atenei Parmensis. 2020;91(1):157-60. WHO. WHO Coronavirus (COVID-19) Dashboard. Geneva: WHO; 2022. Mifsud S, Schembri EL, Gruppetta M. Stress-induced hyperglycaemia. Br J Hosp Med (Lond). 2018;79(11):634-9. Yang J-K, Zhao M-M, Jin J-M, Liu S, Bai P, He W, et al. New-onset COVID-19–related diabetes: an early indicator of multi-organ injury and mortally of SARS-CoV-2 infection. Current Medicine. 2022;1(1):6. Khunti K, Del Prato S, Mathieu C, Kahn SE, Gabbay RA, Buse JB. COVID-19, Hyperglycemia, and New-Onset Diabetes. Diabetes care. 2021;44(12):2645-55. Xue T, Li Q, Zhang Q, Lin W, Wen J, Li L, Chen G. Blood glucose levels in elderly subjects with type 2 diabetes during COVID-19 outbreak: a retrospective study in a single center. Medrxiv. 2020:2020.03. 31.20048579. Haymana C, Demirci I, Tasci I, Cakal E, Salman S, Ertugrul D, et al. Clinical outcomes of non-diabetic COVID-19 patients with different blood glucose levels: a nationwide Turkish study (TurCoGlycemia). Endocrine. 2021;73(2):261-9. Ilias I. Novel appearance of hyperglycemia/diabetes, associated with COVID-19. World journal of virology. 2022;11(2):111-2. Michalakis K, Ilias I. COVID-19 and hyperglycemia/diabetes. World J Diabetes. 2021;12(5):642-50. Yang JK, Feng Y, Yuan MY, Yuan SY, Fu HJ, Wu BY, et al. Plasma glucose levels and diabetes are independent predictors for mortality and morbidity in patients with SARS. Diabet Med. 2006;23(6):623-8. Reiterer M, Rajan M, Gómez-Banoy N, Lau JD, Gomez-Escobar LG, Gilani A, et al. Hyperglycemia in Acute COVID-19 is Characterized by Adipose Tissue Dysfunction and Insulin Resistance. medRxiv. 2021. Juul S, Nielsen EE, Feinberg J, Siddiqui F, Jørgensen CK, Barot E, et al. Interventions for treatment of COVID-19: Second edition of a living systematic review with meta-analyses and trial sequential analyses (The LIVING Project). PloS one. 2021;16(3):e0248132. Mueller AL, McNamara MS, Sinclair DA. Why does COVID-19 disproportionately affect older people? Aging. 2020;12(10):9959-81. WHO. SURVEY TOOL AND GUIDANCE. 2020. Le VT, Ha QH, Tran MT, Le NT, Le VT, Le MK. Hyperglycemia in Severe and Critical COVID-19 Patients: Risk Factors and Outcomes. Cureus. 2022;14(8):e27611. Ad’hiah AH, Al-Bayatee NT, Ahmed AA. Coronavirus disease 19 and risk of hyperglycemia among Iraqi patients. Egyptian Journal of Medical Human Genetics. 2021;22(1):82. Mohan M, Perry BI, Saravanan P, Singh SP. COVID-19 in people with schizophrenia: potential mechanisms linking schizophrenia to poor prognosis. Frontiers in Psychiatry. 2021;12:666067. She ZG, Zhu L, Cheng X, Qin JJ, Zhang XJ, Cai J, et al. Association of Blood Glucose Control and Outcomes in Patients with COVID-19 and Pre-existing Type 2 Diabetes. Cell Metab. 2020;31(6):1068-77.e3. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054-62. Mazori AY, Bass IR, Chan L, Mathews KS, Altman DR, Saha A, et al. Hyperglycemia is associated with increased mortality in critically ill patients with COVID-19. Endocrine Practice. 2021;27(2):95-100. Belice T, Demir I. The gender differences as a risk factor in diabetic patients with COVID-19. Iranian journal of microbiology. 2020;12(6):625-8. Mehraeen E, Karimi A, Barzegary A, Vahedi F, Afsahi AM, Dadras O, et al. Predictors of mortality in patients with COVID-19–a systematic review. European journal of integrative medicine. 2020;40:101226. Singh AK, Gupta R, Ghosh A, Misra A. Diabetes in COVID-19: Prevalence, pathophysiology, prognosis and practical considerations. Diabetes Metab Syndr. 2020;14(4):303-10. Jung C, Wernly B, Fjølner J, Bruno RR, Dudzinski D, Artigas A, et al. Steroid use in elderly critically ill COVID-19 patients. European respiratory journal. 2021;58(4). Sachdeva S, Desai R, Gupta U, Prakash A, Jain A, Aggarwal A. Admission hyperglycemia in non-diabetics predicts mortality and disease severity in COVID-19: a pooled analysis and meta-summary of literature. SN comprehensive clinical medicine. 2020;2:2161-6. Wu J, Huang J, Zhu G, Wang Q, Lv Q, Huang Y, et al. Elevation of blood glucose level predicts worse outcomes in hospitalized patients with COVID-19: a retrospective cohort study. BMJ Open Diabetes Research and Care. 2020;8(1):e001476. Sharma P, Behl T, Sharma N, Singh S, Grewal AS, Albarrati A, et al. COVID-19 and diabetes: Association intensify risk factors for morbidity and mortality. Biomed Pharmacother. 2022;151:113089. Ceriello A. Hyperglycemia and COVID-19: What was known and what is really new? Diabetes research and clinical practice. 2020;167:108383. Cariou B, Hadjadj S, Wargny M, Pichelin M, Al-Salameh A, Allix I, et al. Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: the CORONADO study. Diabetologia. 2020;63(8):1500-15. Sardu C, D'Onofrio N, Balestrieri ML, Barbieri M, Rizzo MR, Messina V, et al. Outcomes in Patients With Hyperglycemia Affected by COVID-19: Can We Do More on Glycemic Control? Diabetes care. 2020;43(7):1408-15. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 May, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 13 Mar, 2026 Editor invited by journal 09 Mar, 2026 Submission checks completed at journal 07 Mar, 2026 First submitted to journal 07 Mar, 2026 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9012176","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630966174,"identity":"a9f8e312-6ed0-4ee0-9533-a4ce416ac91c","order_by":0,"name":"PAUL MUTOO BUKHOTA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYHACxgNgipn54AMgxcNHjB6gFgMGBna2ZAOQFjbitfDzmEmAeAS1mLOfMTh0s+2PHD8zg1nl1xw7GTYG5oePbuDRYtmTY3A4t83AWLKZIe227LZkoMPYjI1z8GgxOADRkrjhMMOx25LbmIFaeNik8Wo5/wamhbGtWHJbPRFabsBtYWZj/LjtMGEtljOeFRzOOWcM9AsbszTjtuM8bMwE/GLOn7zxcU6ZnBw///mPH39uq7bnZ29++Bivw5A5zDxgEo9yDC2MPwioHgWjYBSMgpEJAF6xRLU3ewptAAAAAElFTkSuQmCC","orcid":"","institution":"Mbale Regional Referral Hospital","correspondingAuthor":true,"prefix":"","firstName":"PAUL","middleName":"MUTOO","lastName":"BUKHOTA","suffix":""},{"id":630966188,"identity":"12d5dd1b-f9fe-4016-b6e0-21186cb2f0ac","order_by":1,"name":"MOSES KIRYA","email":"","orcid":"","institution":"Busitema University","correspondingAuthor":false,"prefix":"","firstName":"MOSES","middleName":"","lastName":"KIRYA","suffix":""},{"id":630966192,"identity":"981af71b-49fd-426c-951f-4f61f92d8909","order_by":2,"name":"DENIS BWAYO","email":"","orcid":"","institution":"Busitema University","correspondingAuthor":false,"prefix":"","firstName":"DENIS","middleName":"","lastName":"BWAYO","suffix":""},{"id":630966207,"identity":"268f35ab-768f-4fad-9113-699137f5146e","order_by":3,"name":"JOHN PETER MASABA","email":"","orcid":"","institution":"Busitema University","correspondingAuthor":false,"prefix":"","firstName":"JOHN","middleName":"PETER","lastName":"MASABA","suffix":""},{"id":630966212,"identity":"ab368581-3cc4-46ec-b575-f1ac8408ec1d","order_by":4,"name":"ASAD BUYINDA","email":"","orcid":"","institution":"Mbale Regional Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"ASAD","middleName":"","lastName":"BUYINDA","suffix":""},{"id":630966216,"identity":"bc29b6d4-1cd2-4845-8d27-c1597b9f66b8","order_by":5,"name":"ISMAIL MUTEGEKI","email":"","orcid":"","institution":"Busitema University","correspondingAuthor":false,"prefix":"","firstName":"ISMAIL","middleName":"","lastName":"MUTEGEKI","suffix":""},{"id":630966217,"identity":"fa4cddf4-1f59-49b3-9682-fde063b72619","order_by":6,"name":"JOHN BOSCO TWINE","email":"","orcid":"","institution":"Busitema University","correspondingAuthor":false,"prefix":"","firstName":"JOHN","middleName":"BOSCO","lastName":"TWINE","suffix":""},{"id":630966229,"identity":"0f8d7894-fb06-4b2c-97c9-a34f277d99d6","order_by":7,"name":"PETER OLUPOT-OLUPOT","email":"","orcid":"","institution":"Busitema University","correspondingAuthor":false,"prefix":"","firstName":"PETER","middleName":"","lastName":"OLUPOT-OLUPOT","suffix":""},{"id":630966233,"identity":"29fc0d91-2eb9-4f6c-b80d-973b4bcac767","order_by":8,"name":"RICHARD KATURAMU","email":"","orcid":"","institution":"Busitema University","correspondingAuthor":false,"prefix":"","firstName":"RICHARD","middleName":"","lastName":"KATURAMU","suffix":""}],"badges":[],"createdAt":"2026-03-02 16:11:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9012176/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9012176/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108385602,"identity":"1fd5ba2e-8100-498f-84fd-c7dd8023aa39","added_by":"auto","created_at":"2026-05-04 06:04:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence of hyperglycaemia among COVID-19 patients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9012176/v1/8c792fa298e0b68d93fd29a9.png"},{"id":108493312,"identity":"7ccad437-8da9-424b-a966-87d1f280fb76","added_by":"auto","created_at":"2026-05-05 09:59:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26777,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence of hyperglycaemia by DM status among COVID-19 patients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9012176/v1/c0bfe3f8dbb932f35e6416be.png"},{"id":108495294,"identity":"e37d492e-1e9e-462d-aac6-2232389a4e23","added_by":"auto","created_at":"2026-05-05 10:09:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":492667,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9012176/v1/c04e9a7b-377b-4c27-b6d2-3824b28347f7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCovid-19 a Strong Predictor of Hyperglycaemia Among Ugandan Patients: A Retrospective Study\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eCoronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). COVID-19 was declared a pandemic by the World Health Organization (WHO) on March 11, 2020, in the Republic of China (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Nearly 509,531,232\u0026nbsp;million COVID-19 cases had been confirmed globally by August 2023, with 770,437,327\u0026nbsp;million fatalities (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to the American Association of Diabetes and the American Association of Clinical Endocrinologists consensus, hospital-developed hyperglycaemia is defined as fasting capillary blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.8 mmol/L in a patient with no history or evidence of diabetes, or random capillary blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L in diabetic patients. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Hyperglycaemia was commonly observed among patients admitted with COVID-19 who did not have a prior history of diabetes mellitus and were not using glucocorticoids. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Additionally, several studies found COVID-19 infection to be associated with the development of hyperglycaemia or new on-set type 2 diabetes mellitus (DM) (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). It has been observed that the prevalence of COVID-19-associated hyperglycaemia with or without pre-existing type 2 diabetes mellitus is 25%, while associated new-onset DM is 19% (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Data further shows that both critically and non-critically ill COVID-19 patients present with higher than expected blood glucose levels, even in the absence of DM (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong COVID-19 patients, some studies have found hyperglycaemia to be an independent predictor of mortality and morbidity (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The risk of all causes of death in COVID-19 patients with hyperglycaemia is nearly double compared to that of patients with pre-existing DM (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Furthermore, hyperglycaemia has been associated with a prolonged hospital stay, often requiring life support with intensive care, mechanical ventilation, and renal replacement therapy (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Additionally, poor quality of life and non-serious adverse events have also been reported (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). It has been postulated that apart from comorbidities associated with aging, such as hypertension, chronic lung disease, and diabetes, COVID-19 with hyperglycaemia could explain the poor outcomes among elderly COVID-19 patients (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are limited data on COVID-19-associated hyperglycaemia in Sub-Saharan Africa. The only study to assess the prevalence of hyperglycaemia in COVID-19 patients was conducted in South Africa, specifically KwaZulu-Natal, and found that 9.3% of admitted patients had hyperglycaemia (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This study did not explore the association between different characteristics of COVID-19 patients and hyperglycaemia.\u003c/p\u003e \u003cp\u003eThis study aimed to investigate the prevalence of hyperglycaemia, associated factors among admitted COVID-19 patients in eastern Uganda.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis was a retrospective cross sectional study conducted between 1st March 2020 and 31st December 2021.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy site\u003c/h2\u003e \u003cp\u003e The study was carried out on admitted adult COVID-19 patients at Mbale and Soroti regional referral hospital COVID-19 treatment centres. Mbale and Soroti Regional Referral Hospitals (RRHs) are the highest-tier public referral health facilities located in Eastern Uganda. These two hospitals are among the 13 regional referral hospitals in Uganda. Mbale Regional Referral Hospital (MRRH) is situated at the heart of Mbale City, while Soroti Referral Hospital (SRRH) is a government hospital located in Soroti city in eastern Uganda. More than 90% of the population live in rural communities.\u003c/p\u003e \u003cp\u003e Mbale and Soroti regional referral hospitals were designated by the Ugandan Ministry of Health as COVID-19 treatment centres to admitted and manage COVID-19 patients in March 2020 when COVID-19 was declared a pandemic.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe study population included confirmed COVID-19 adult patients admitted in the catchment area of Mbale and Soroti regional referral hospital COVID-19 treatment centres during the period of the study. The Inclusion criteria included all confirmed COVID-19 cases with either positive reverse transcription polymerase chain reaction (RT-PCR) or positive RDT results, COVID-19 patients with documented test results for capillary Blood Sugar at two separate times during the hospital stay and 18 years and above including patients with complete admission files and records while exclusion criteria patients file with a single recording of blood sugar level or no results. All files of COVID-19 patients admitted to these hospitals from 1st March 2020 to 31st December 2021 were retrieved from their archives. A checklist with eligibility criteria was applied to each file.\u003c/p\u003e\n\u003ch3\u003eSampling size and Sampling procedure\u003c/h3\u003e\n\u003cp\u003eThe medical records with confirmed COVID-19 patients in the archives of Mbale and Soroti Regional Referral Hospital COVID-19 treatment centres were accessed. This was done by sampling of target files with COVID-19 patients above 18 years within the two treatment centres. We used capillary blood glucose (BG), because it is the most commonly used method in clinical practice for blood sugar level monitoring in our setting.\u003c/p\u003e \u003cp\u003eAll files of COVID-19 patients admitted to these hospitals from 1st March 2020 to 31st December 2021 were retrieved from their archives. A checklist with eligibility criteria was applied to each file. Files with either, COVID-19 PCR or RDT results and capillary blood glucose measurements on at least two separate occasions were selected for data abstraction. Subsequently, all files were returned to the hospital archives\u003c/p\u003e \u003cp\u003eInfection control procedures such as social distancing, hand washing, and wearing face masks were observed during the data collection period.\u003c/p\u003e\n\u003ch3\u003eData collection methods\u003c/h3\u003e\n\u003cp\u003eA data abstraction tool was developed to collect information from patient charts. The tool was adapted from the WHO-modified stepwise questionnaire(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), adopted and contextualised to Ugandan context to collect data on demographics such as age, sex, tribe, education, employment, marital status, residence, weight, laboratory results, biophysical profile, COVID-19 status, and treatment status (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTrained research assistants used the data collection tool to collect data. To obtain accurate and complete information from the COVID-19 patients' files, we trained four research assistants on how to use the selection criteria accurately, record data from the admission forms. The research teams were also trained in infection prevention and control measures for COVID-19, and they used facemasks, eye shields, and hand sanitizers during and after reviewing documents. The principal investigator supervised the data collection exercise from beginning to end, ensuring that every file was checked to confirm the accuracy of entries.\u003c/p\u003e \u003cp\u003eThe primary dependable variable was hyperglycaemia. It is a binary either a patient has hyperglycaemia or not recorded as YES for hyper and NO for no hyper\u003c/p\u003e \u003cp\u003eSocio-demographic: Age, weight, height, Education level, Religion, Tribe, Marital status, Occupation, and clinical characteristics: Co-morbidities, type of drugs, clinical features, treatment, symptoms, laboratory results (capillary blood glucose) were the predictor variables.\u003c/p\u003e \u003cp\u003eTrained research assistants extracted data on key variables of interest from confirmed COVID-19 patients' case files into the study questionnaire. The abstracted data included socio-demographic characteristics, clinical features, laboratory results (Capillary Blood Glucose), treatments administered, and clinical outcomes (death or discharge). The data was then entered into an electronic database using Google Forms.\u003c/p\u003e \u003cp\u003eCompleted data tools were reviewed daily by the principal investigator, and all completed tools were stored in a secure location. No names were used as identifiers on the data collection tools. Data capture screens with built-in checks for consistency, logical flow, range, and accuracy of data were designed in Epi-data version 5 and used for electronic data capture (data entry). Data entry was conducted at a secure office at the Busitema University College of Health Sciences, and all data were double-entered. All electronic data was saved on a password-protected external drive. All hard copies of the completed data collection tools were secured under lock and key and were only accessed by key data management team members.\u003c/p\u003e \u003cp\u003eEpi data version 5 was used for data entry. We also used Stata 15 for cleaning and editing our data before data analysis. Once extracted into Excel, the data were sorted to identify exclusion criteria, notably incomplete data (mainly without clinical outcome status). Data processing\u003c/p\u003e \u003cp\u003eThe data were cleaned for errors and omissions at all stages of data processing. To ensure data quality, double-entry was performed to ensure correct data entry. Subsequently, the data were exported to Stata Version 15, where sorting, categorization, and necessary variable transformations were conducted.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData analysis:\u003c/h2\u003e \u003cp\u003eThe data were analyzed using Stata (version 15.0, College Station, Texas 77845 USA). Proportions and percentages were determined for categorical variables at the univariate and bivariate levels of analysis. The prevalence of hyperglycaemia in COVID-19 patients was determined and reported as a percentage with 95% confidence intervals. Univariate analysis was used to summarize the clinical and demographic characteristics of our study population. For bivariate analysis, differences between continuous variables were determined using Student\u0026rsquo;s t-test for normally distributed variables, while differences between categorical variables were determined using the Chi-square test as appropriate.\u003c/p\u003e \u003cp\u003eThe 95% confidence interval (CI) was set, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. Associations between the dependent variable (hyperglycemia) and independent variables (sociodemographic and clinical characteristics) were initially assessed by bivariate analysis. Multivariate regression models were fitted for all clinical and demographic variables to control for confounding and identify prognostic factors, using death as the outcome variable with a significance level of p\u0026thinsp;=\u0026thinsp;0.05. In the multivariate analyses, the independent variables were also checked for interaction with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/p\u003e \u003cp\u003eSignificant results from the bivariate analysis were used in the multivariate analysis to determine their independent effects on the dependent variables. Results were interpreted using adjusted odds ratios (aORs) and 95% CI. To describe the outcome, a risk analysis was performed using multivariate regression analysis with death as the outcome variable and clinical and demographic characteristics, including hyperglycaemia, as the independent variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOut of 1865 files retrieved from the archive of extracted from both Mbale and Soroti Regional Referral Hospital COVID-19 treatment centres, we extracted retrieved 766 patients\u0026rsquo; files and obtain full records for 711 patients\u0026rsquo; files were included in this study. The median age of participants was 58 years, interquartile range (IQR) 18 to 98 years. The median age associated with onset of hyperglycemia 57 and interquartile range was 18 to 98.\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\u003eSocio-demographic characteristics of patients with COVID-19 with hyperglycaemia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal n\u0026thinsp;=\u0026thinsp;711(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHyperglycaemia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo n\u0026thinsp;=\u0026thinsp;252(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes n\u0026thinsp;=\u0026thinsp;459(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e333(46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119(47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e214(46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e378(53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133(52.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e245(53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02(0.8, 1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (Years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44(6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38(8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69(9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58(12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8(0.3, 2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118(16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66(14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2(0.1, 0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e152(21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58(23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94(20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3(0.1, 0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e328(46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125(49.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e203(44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3(0.1, 0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of education completed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170(23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e157(34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e277(39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137(54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e140(30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1(0.04, 0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116(16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51(20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65(14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1(0.1, 0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e148(20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51(20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97(21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2(0.1, 0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94(13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60(13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1(0.7,1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormal employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155(21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60(23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95(20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeasant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e360(50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140(55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e220(47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0(0.7, 1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21(3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.6(1.7, 96.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81(11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17(6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64(13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4(1.3, 4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e527(74.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e204(81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e323(70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e184(25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48(19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e136(29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8(1.2, 2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eA known DM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e497(69.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102(40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e395(86.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.1(6.3, 13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSocio-demographic characteristics of COVID-19 patients with hyperglycaemia\u003c/h3\u003e\n\u003cp\u003eAs shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e there were more males, 53.2% (378/711) and majority 46.1% (328/711) of the patients were aged over 60 years. The majority lived in rural 74.1% (527/711) and were peasants 50.6% (360/711).\u003c/p\u003e \u003cp\u003eHyperglycaemia increased with age. The proportion of patients with hyperglycaemia was highest in those more than 60 years with 44.2% (203/459)\u003c/p\u003e \u003cp\u003eThe proportion of patients with hyperglycaemia was highest in those people who had no education at 34.2% (157/459) and lowest in those who had the highest level of secondary education at 14.2% (65/459).\u003c/p\u003e \u003cp\u003eMore peasants were more likely to have hyperglycaemia with a proportion of 47.9% (220/459) while few students presented with hyperglycaemia with a proportion of 4.4% (20/459).\u003c/p\u003e \u003cp\u003eThere was also a statistically significant association between hyperglycaemia and place of residence. There was a high proportion of patients from rural areas presenting with hyperglycaemia, 70.4% (323/459) compared to those from urban areas 29.6% (136/459) who were in the rural areas were more likely to present with hyperglycaemia compared to those in Urban area.\u003c/p\u003e\n\u003ch3\u003ePrevalence of hyperglycaemia among COVID-19 patients\u003c/h3\u003e\n\u003cp\u003eThe overall prevalence of hyperglycaemia among COVID-19 patients was found at (65%: 95%CI 0.6\u0026ndash;0.7) (459/771) as shown in Fig.\u0026nbsp;1 below. However, among those with no history of known DM, the hyperglycaemia prevalence was 46.2% (153/331) while that with known DM it was at 80.5% (306/380) on admission as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1: Prevalence of hyperglycaemia among COVID-19 patients\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of COVID-19 patients with hyperglycaemia\u003c/h2\u003e \u003cp\u003eBivariate analysis of clinical characteristics, considering the independent variable and hyperglycaemia using logistic regression, found that having a medical history of diabetes mellitus (COR\u0026thinsp;=\u0026thinsp;4.8, 95% CI: 3.4\u0026ndash;6.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hypertension (COR\u0026thinsp;=\u0026thinsp;2.3, 95% CI: 1.6\u0026ndash;3.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), current use of steroids (COR\u0026thinsp;=\u0026thinsp;2.9, 95% CI: 1.8\u0026ndash;4.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and dextrose infusion (COR\u0026thinsp;=\u0026thinsp;1.8, 95% CI: 1.1\u0026ndash;2.9, p\u0026thinsp;=\u0026thinsp;0.023) before admission were significantly associated with the development of hyperglycemia among COVID-19 patients at a 95% confidence interval (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, presenting with clinical features such as feeling tired or fatigued or weakness (COR\u0026thinsp;=\u0026thinsp;2.6, 95% CI: 1.5\u0026ndash;4.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), weight loss (COR\u0026thinsp;=\u0026thinsp;0.4, 95% CI: 0.3\u0026ndash;0.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), headache or blurred vision (COR\u0026thinsp;=\u0026thinsp;0.3, 95% CI: 0.2\u0026ndash;0.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), fever (COR\u0026thinsp;=\u0026thinsp;1.8, 95% CI: 1.1\u0026ndash;2.9, p\u0026thinsp;=\u0026thinsp;0.020), abdominal pain (COR\u0026thinsp;=\u0026thinsp;8.5, 95% CI: 1.1\u0026ndash;64.6, p\u0026thinsp;=\u0026thinsp;0.014), and coma (COR\u0026thinsp;=\u0026thinsp;2.8, 95% CI: 1.8\u0026ndash;4.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also statistically significantly associated with hyperglycemia among COVID-19 patients.\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\u003eClinical characteristics of COVID-19 patients that developed hyperglycaemia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal n\u0026thinsp;=\u0026thinsp;711\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHyperglycaemia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo n\u0026thinsp;=\u0026thinsp;252(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes n\u0026thinsp;=\u0026thinsp;459(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Mellitus(n\u0026thinsp;=\u0026thinsp;658)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e380(57.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74(29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e306(66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.8(3.4, 6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216(30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50(19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166(36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3(1.6, 3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echronic heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1(0.7, 5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12(2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.7(0.9, 52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echronic lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7(0.3, 8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echronic liver disease(\u003cem\u003en\u0026thinsp;=\u0026thinsp;704)*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.9(0.5, 31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent treatment before Admission\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteroids(n\u0026thinsp;=\u0026thinsp;543) \u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132(18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24(9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108(23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9(1.8, 4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntipsychotics(n\u0026thinsp;=\u0026thinsp;544) \u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.9(0.5, 31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDextrose infusions(n\u0026thinsp;=\u0026thinsp;538) \u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24(9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72(15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8(1.1, 2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiazide diuretics(n\u0026thinsp;=\u0026thinsp;540) \u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7(0.2, 2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParenteral/Enteral nutrition(n\u0026thinsp;=\u0026thinsp;545) \u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4(0.7, 8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-retroviral therapy(n\u0026thinsp;=\u0026thinsp;541) \u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent Treatment for DM during Admission\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e292(41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94(37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e198(43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3(0.9, 1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral anti-hyperglycemic agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91(12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24(9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67(14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6(0.9, 2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSymptoms\u003c/b\u003e(n\u0026thinsp;=\u0026thinsp;704) \u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequent urination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6(0.6, 4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncreased thirst(polydipsia)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8(0.2, 2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncreased appetite (polyphagia)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e417(58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198(78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e219(47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2(0.2, 0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeeling tired or fatigued or weakness(n\u0026thinsp;=\u0026thinsp;702) \u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68(14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.6(1.5, 4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eweight loss(n\u0026thinsp;=\u0026thinsp;702) \u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e512(72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e205(81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e307(66.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4(0.3, 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache or/ Blurred vision(n\u0026thinsp;=\u0026thinsp;701) \u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e496(69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e210(83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e286(62.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3(0.2, 0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever(n\u0026thinsp;=\u0026thinsp;705) \u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100(14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25(9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75(16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8(0.1, 2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal pain(n\u0026thinsp;=\u0026thinsp;699) \u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15(3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.5(1.1, 64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComa(n\u0026thinsp;=\u0026thinsp;699) \u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27(10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e115(25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8(1.8, 4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShortness of breath(n\u0026thinsp;=\u0026thinsp;704) \u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26(5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e*Some data is missing. \u003csup\u003e1\u003c/sup\u003eMultiple response is possible\u003c/h2\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe study found the prevalence of hyperglycaemia among COVID-19 patients to be 459/711(65%). This is closely similar to other reported studies. A retrospective observational study by viet et al 2022 among 517 COVID-19 adults with hyperglycemia in severe and critical COVID-19 patients in field hospital showed a prevalence of 65.6%(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This could be attributed to several factors including the study design since we used a retrospective cross sectional study basing on hospital records. Further cross sectional observational study by Ad\u0026rsquo;hiah et al 2021 among 213 COVID-19 patients reported 22.5% prediabetes and 52.1% diabetes in patients without prior history of diabetes(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) It has been reported by Mohan et al (2021) in Korea among 7341 patients with COVID-19 can lead to glucose dysregulation and induce hyperglycaemia through various mechanisms like inflammation, stress, and direct effects on pancreatic cells (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, our study also examined the prevalence of hyperglycaemia among COVID-19 patients based on their diabetic status. Among unknown non-DM COVID-19 patients, the prevalence of hyperglycaemia was found to be 46.2%. In contrast, known DM COVID-19 patients had a higher prevalence of hyperglycaemia at 80.5%. These findings align with previous research highlighting that individuals with pre-existing diabetes are more likely to experience hyperglycaemia. A cross sectional study among 7,337 patients with COVID-19 in Hubei Province, China in 2020 by She, Zhu (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) showed that the interaction between COVID-19 and diabetes can worsen glycemic control, leading to increased complications and 7.8% mortality. Another study by Zhu et al. (2020) conducted in China reported similar findings, indicating that hyperglycaemia was more prevalent in diabetic COVID-19 patients compared to non-diabetic patients with an unknown diabetic status. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, a 46.2% prevalence of hyperglycaemia in previously unknown non-diabetic patients is unexpectedly high. This finding highlights the increased need for monitoring COVID-19 patients, regardless of their medical history of diabetes. The occurrence of hyperglycaemia in non-diabetic COVID-19 patients could be attributed to the reported phenomenon of stress-induced hyperglycaemia, where the body responds to trauma, infections, or sepsis by increasing blood sugar levels through the activation of various neuroendocrine pathways (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to our findings, approximately 53.2% of the patients included in the study were male, indicating a slightly higher representation of males in the study population. This is also similar to a retrospective single centre study by Mazori et al. (2021) conducted among 133 critically ill COVID-19 patients at an Urban academic quaternary care centre, which found that 69% were male patients (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Another study from Turkey by Tahir Belice et al. revealed that diabetic men had a higher risk of mortality (men 40.9% and women 18.2%), and the rates of admission were higher for men with COVID-19 compared to women (women 17.1% versus men 47.4%) than for other diseases (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).. The reason for this gender distribution could be attributed to various factors, such as differences in exposure to risk factors, or variations in susceptibility to COVID-19 between males and females.\u003c/p\u003e \u003cp\u003eWe found that majority, 46.1% of the patients, were over 60 years old. Older age has been consistently identified as a risk factor for severe COVID-19 outcomes in numerous studies worldwide. A retrospective cohort study conducted by Zhou et al. (2020) in Jinyintan and Wuhan pulmonary hospital China on 191 COVID-19 patients reported that advanced age was associated with higher mortality rates 36% among COVID-19 patient (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis is also similar to a study by Mazori et al. (2021) conducted among critically ill COVID-19 patients in Malaysia, which found that older age was significantly associated with hyperglycaemia in COVID-19 patients. The prevalence of hyperglycaemia was much higher among older patients 46.6% compared to younger ones (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Physiological changes associated with aging, such as decreased insulin sensitivity and impaired glucose regulation, contribute to higher glucose levels. Therefore, the higher prevalence of older individuals in the study could reflect the increased vulnerability of this age group to severe COVID-19 disease.\u003c/p\u003e \u003cp\u003eThe majority of the patients lived in rural areas 70.4% and were peasants. Similar findings have been reported in other studies. A systematic review by Mehraeen et al. (2020) showed a higher pooled prevalence of 69.8% of hyperglycaemia among COVID-19 patients living in rural areas compared to those living in urban areas. These findings suggest a higher representation of individuals from rural settings and an occupation primarily associated with agricultural work. Rural populations may face unique challenges in terms of access to healthcare facilities, resources, and information. Limited access to quality healthcare in rural areas could potentially impact the detection, management, and outcomes of hyperglycaemia associated with COVID-19. This is in agreement with Mehraeen et al., 2020 studies suggested that limited access to healthcare resources and disparities in healthcare services in rural areas might have contributed to this association (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study identified at the following characteristics; history of DM, hypertension, and cerebrovascular disease. It also reported high prevalence rates of hyperglycaemia in COVID-19 patients with specific chronic illnesses, such as chronic kidney disease. Similar findings were reported by Singh et al. (2020) in a retrospective study conducted in India among 1093 admitted COVID-19 patients in a referral hospital, which found that a history of DM and hypertension were independent risk factors for the development of hyperglycaemia in COVID-19 patients (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, the current use of steroids before admission was also associated with the development of hyperglycaemia. This could be due to the effects of steroids on the body's glucose metabolism; steroids have been reported to increase the body's mobilization of blood sugar levels. These findings are consistent with other studies conducted in different populations. Similarly, a systematic review by Jung et al. (2021) in Pakistan, where they conducted aggregate data meta-analyses, trial sequential analyses, network meta-analyses, and individual patient data meta-analyses on 81 clinical trials, identified steroid use as a significant predictor of hyperglycaemia in COVID-19 patients (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Dextrose contains glucose, so its use directly raises blood sugars. This implies that cautious use of steroids and dextrose in COVID-19 patients is required for good clinical outcomes.\u003c/p\u003e \u003cp\u003eFurthermore, the use of certain other medications, including antiretroviral therapy, antipsychotics, and parenteral nutrition, was associated with a higher prevalence of hyperglycaemia, although the prevalence was relatively lower in patients treated with insulin compared to those treated with oral hypoglycemic agents.\u003c/p\u003e \u003cp\u003ePresenting clinical features such as feeling tired or fatigued, weakness, weight loss, headache or blurred vision, fever, abdominal pain, coma, shortness of breath, and dehydration were significant. Similar findings have been reported in other studies. For instance, a study by Sachdeva et al. (2020) conducted in India found an association between fatigue, weight loss, and hyperglycaemia in COVID-19 patients. The study also reported an increased prevalence of hyperglycaemia in patients with fever and respiratory symptoms (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Additionally, a retrospective study by Wu et al. (2020) in China found that the elevation of blood glucose levels predicts worse outcomes in hospitalized patients with COVID-19. They identified symptoms such as fatigue, dyspnea, and dehydration as indicators of severe COVID-19 and potential risk factors for hyperglycaemia (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). These clinical symptoms are nonspecific, as they can also occur in other disease conditions. This implies that the detection of hyperglycaemia among COVID-19 patients is not possible solely by clinical symptoms, but rather requires blood sugar level monitoring\u003c/p\u003e \u003cp\u003eThe study reported high prevalence rates of hyperglycaemia in COVID-19 patients with specific chronic illnesses, such as cerebrovascular disease, and chronic kidney disease. Furthermore, the use of certain medications, including antiretroviral therapy, antipsychotics, steroids, and parenteral nutrition, was associated with a higher prevalence of hyperglycaemia. However, we could not explain why patients on antipsychotics, and parenteral nutrition were associated with hyperglycaemia. The prevalence was relatively lower in patients treated with insulin compared to those treated with oral hypoglycemic agents. This difference could be explained by the fact that insulin is more potent than oral hypoglycemic agents in controlling blood sugar.\u003c/p\u003e \u003cp\u003eThe study found that living in rural areas was associated with a higher risk of developing hyperglycaemia in COVID-19 patients. These findings are consistent with those of Sharma et al. (2021) in the United Kingdom, who demonstrated that urban residency was associated with an increased risk of developing hyperglycaemia among COVID-19 patients (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). One possible explanation for this higher risk could be that urban areas often have different lifestyles and dietary habits compared to rural areas. Urban populations may have a higher consumption of processed foods, junk food, sugary beverages, and sedentary lifestyles, which are known risk factors for hyperglycaemia and diabetes. These unhealthy habits may exacerbate the impact of COVID-19 on glucose regulation and increase the likelihood of hyperglycaemia in infected patients. A study by Mehraeen et al. (2020) conducted in Iran found that living in rural areas was associated with a lower risk of hyperglycaemia in COVID-19 patients (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe COVID-19 patients who had a medical history of diabetes mellitus had 4.8 times increased odds of developing hyperglycaemia. The presence of diabetes mellitus can impair glucose regulation, and when coupled with COVID-19 disease, which is a stressor to the body, individuals are more susceptible to hyperglycaemia. These findings align with the existing body of evidence, emphasizing the importance of closely monitoring and managing blood glucose levels in COVID-19 patients with a history of diabetes mellitus. Numerous studies have reported that pre-existing diabetes is a significant risk factor for hyperglycaemia in individuals with COVID-19 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe current use of steroids before admission was associated with 2.9 times higher odds of developing hyperglycaemia in COVID-19 patients. Steroid medications, such as dexamethasone, have been widely used in the treatment of severe COVID-19 cases to reduce inflammation and improve outcomes. However, steroids can also lead to hyperglycaemia by increasing the catabolism of different body metabolites, thereby increasing blood glucose levels. Several studies have reported that steroids, particularly high-dose glucocorticoids, can induce or exacerbate hyperglycaemia by increasing insulin resistance and impairing glucose tolerance (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). These findings have been observed in studies conducted in various populations and settings, including both hospital and community settings. The study also found that COVID-19 patients who were aged 40 years and older and had formal education were associated with a 0.2 lower risk of developing hyperglycaemia. Older age (40 years and older) and formal education may reflect better overall health literacy and a higher likelihood of practicing healthy lifestyle behaviours, which could contribute to better glucose regulation.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStudy strengths\u003c/h2\u003e \u003cp\u003eThe strengths of this study, it\u0026rsquo;s the first documented study that assessed hyperglycemia among COVID-19 patients in two public hospitals both urban and rural settings in eastern Uganda thus our results are representative of a bigger population of hospitalized COVID-19 patients in the country.\u003c/p\u003e \u003cp\u003eThe sample size was big enough and powered enough to identify potential associations between the variables of interest.\u003c/p\u003e \u003cp\u003eThis study looked at capillary blood glucose levels to arrive at the overall prevalence outside ICU settings, compared to several studies that looked at ICU patients.\u003c/p\u003e \u003cp\u003eThe research provides potential information in the development of clinical protocol on routine screening for hyperglycemia during epidemics or even pandemics to cater for routine screening and as well as timely assessment and treatment of hyperglycemia and its complications in COVID-19 patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations\u003c/h2\u003e \u003cp\u003eThe retrospective nature of the study, obtaining all the data in relation to parameters of interest was not possible. So the results might have been subject to under reporting\u003c/p\u003e \u003cp\u003eThere was missing information in some patient\u0026rsquo;s files which is a challenge especially the patients who were transferred out of the participating hospitals.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe prevalence of hyperglycaemia among hospitalised patients was high in our study.\u003c/p\u003e \u003cp\u003eFactors associated with development of hyperglycaemia included increasing age, living in rural areas and male gender while clinical characteristics were history of diabetes mellitus and current use of steroids.\u003c/p\u003e \u003cp\u003eHyperglycaemia was found to be an independent risk factor among COVID-19 patients.\u003c/p\u003e \u003cp\u003e We therefore recommend that; standardised guidelines for screening and treating hyperglycaemia in all COVID-19 patients and should be incorporated into the routine care package during epidemics and pandemics.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACE2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Angiotensin-converting enzyme 2\u003c/p\u003e\n\u003cp\u003eCBD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Capillary Blood Glucose\u003c/p\u003e\n\u003cp\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Confidence Interval\u003c/p\u003e\n\u003cp\u003eCOVID-19\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;SARS-cov-2\u003c/p\u003e\n\u003cp\u003eCRP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;C-reactive protein\u003c/p\u003e\n\u003cp\u003eCTU\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;COVID-19 Treatment Unit\u003c/p\u003e\n\u003cp\u003eDKA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Diabetic Ketoacidosis\u003c/p\u003e\n\u003cp\u003eDM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eDNA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;deoxyribonucleic acid\u003c/p\u003e\n\u003cp\u003eFPG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Fasting Plasma Glucose\u003c/p\u003e\n\u003cp\u003eHDL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;High-Density Lipoprotein\u003c/p\u003e\n\u003cp\u003eHDU\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;High dependency unit\u003c/p\u003e\n\u003cp\u003eHHS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Hyperosmolar Hyperglycemic State\u003c/p\u003e\n\u003cp\u003eICU\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intensive Care Unit\u003c/p\u003e\n\u003cp\u003eLDL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Low-Density Lipoprotein\u003c/p\u003e\n\u003cp\u003emmHg\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;millimeter of mercury\u003c/p\u003e\n\u003cp\u003emmol/l\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;millimole per litre\u003c/p\u003e\n\u003cp\u003eMOH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ministry of Health\u003c/p\u003e\n\u003cp\u003eMRRH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Mbale Regional Referral Hospital\u003c/p\u003e\n\u003cp\u003eOR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Odds Ratio\u003c/p\u003e\n\u003cp\u003ePCR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eRBS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Random Blood Sugar\u003c/p\u003e\n\u003cp\u003eRNA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ribonucleic Acid\u003c/p\u003e\n\u003cp\u003eRR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Relative Risk\u003c/p\u003e\n\u003cp\u003eSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Standard Deviation\u003c/p\u003e\n\u003cp\u003eSOPs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Standard Operating Procedures\u003c/p\u003e\n\u003cp\u003eSRRH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Soroti Regional Referral Hospital\u003c/p\u003e\n\u003cp\u003eSSA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sub-Saharan Africa\u003c/p\u003e\n\u003cp\u003eT2D\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Type 2 diabetes mellitus\u003c/p\u003e\n\u003cp\u003eTc\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Total cholesterol\u003c/p\u003e\n\u003cp\u003eTG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Triglyceride\u003c/p\u003e\n\u003cp\u003eUNCST\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Uganda National Council of Science and Technology\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the management of Mbale and Soroti Regional Hospital for permitting us to conduct our study and for their support throughout the data collection period. We are specifically thankful to COVID-91 treatment centres records department who helped us with the data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePMB contributed to the design, analysis, and article write-up. PMB was the principal investigator and conceptualized the overall design, conducted the study, and article write-up. POO contributed to the conceptualization, and overall design, and supervised the research process, analysis, and article write-up. RK supervised the research process at all stages, including analysis and interpretation and article write-up. DB contributed to the restructuring of the paper, and writing up of the article, consented to the publication, and guided the overall design of intervention and article write-up. JPM contributed to the design, and article write-up, and ensured adherence to the research protocols in the implementation, including overall coordination. OA contributed to data entry, data analysis, and interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement;\u0026nbsp;\u003c/strong\u003eResearch reported in this publication was supported by the Fogarty International Centre of the National Institutes of Health, U.S. Department of State’s Office of the U.S. Global AIDS Coordinator and Health Diplomacy (S/GAC), and President’s Emergency Plan for AIDS Relief (PEPFAR) under Award Number 1R25TW011213. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used during our study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Data was collected by reviewing documents with no direct interaction with the patients (patient’s files). The study was approved by the Busitema University Research and Ethics Committee (REC), No. PDF 2022-11-03). We also got administrative clearance from Mbale and Soroti Regional Referral Hospital REC to access the patient's files. All data and study documents were stored securely, according to Good Clinical Practice 10 on secure storage of research materials.\u003c/p\u003e\n\u003cp\u003eWe sought waiver of consent from patients the Busitema University Research and Ethics committee (REC) and was granted, since we were reviewing patient’s records. Throughout the study, we did not use any unique patient identifiers and we complied with the Helsinki Declaration\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\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eAuthor details\u003c/p\u003e\n\u003cp\u003eInternal Medicine at the Faculty of Health Sciences, Busitema University,\u003c/p\u003e\n\u003cp\u003eMbale, Uganda\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWHO. Coronavirus disease (COVID-19) 2021 [cited 2023 28TH/07/2023]. Available from: https://www.who.int/health-topics/coronavirus#tab=tab_1.\u003c/li\u003e\n \u003cli\u003eSynowiec A, Szczepański A, Barreto-Duran E, Lie LK, Pyrc K. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): a Systemic Infection. Clinical microbiology reviews. 2021;34(2).\u003c/li\u003e\n \u003cli\u003eMirzaei F, Khodadadi I, Vafaei SA, Abbasi-Oshaghi E, Tayebinia H, Farahani F. Importance of hyperglycemia in COVID-19 intensive-care patients: Mechanism and treatment strategy. Prim Care Diabetes. 2021;15(3):409-16.\u003c/li\u003e\n \u003cli\u003eORGANISATION WH. WHO STEPS Surveillance Manual 2 October 2020\u003c/li\u003e\n \u003cli\u003eCucinotta D, Vanelli M. WHO Declares COVID-19 a Pandemic. Acta bio-medica : Atenei Parmensis. 2020;91(1):157-60.\u003c/li\u003e\n \u003cli\u003eWHO. WHO Coronavirus (COVID-19) Dashboard. Geneva: WHO; 2022.\u003c/li\u003e\n \u003cli\u003eMifsud S, Schembri EL, Gruppetta M. Stress-induced hyperglycaemia. Br J Hosp Med (Lond). 2018;79(11):634-9.\u003c/li\u003e\n \u003cli\u003eYang J-K, Zhao M-M, Jin J-M, Liu S, Bai P, He W, et al. New-onset COVID-19\u0026ndash;related diabetes: an early indicator of multi-organ injury and mortally of SARS-CoV-2 infection. Current Medicine. 2022;1(1):6.\u003c/li\u003e\n \u003cli\u003eKhunti K, Del Prato S, Mathieu C, Kahn SE, Gabbay RA, Buse JB. COVID-19, Hyperglycemia, and New-Onset Diabetes. Diabetes care. 2021;44(12):2645-55.\u003c/li\u003e\n \u003cli\u003eXue T, Li Q, Zhang Q, Lin W, Wen J, Li L, Chen G. Blood glucose levels in elderly subjects with type 2 diabetes during COVID-19 outbreak: a retrospective study in a single center. Medrxiv. 2020:2020.03. 31.20048579.\u003c/li\u003e\n \u003cli\u003eHaymana C, Demirci I, Tasci I, Cakal E, Salman S, Ertugrul D, et al. Clinical outcomes of non-diabetic COVID-19 patients with different blood glucose levels: a nationwide Turkish study (TurCoGlycemia). Endocrine. 2021;73(2):261-9.\u003c/li\u003e\n \u003cli\u003eIlias I. Novel appearance of hyperglycemia/diabetes, associated with COVID-19. World journal of virology. 2022;11(2):111-2.\u003c/li\u003e\n \u003cli\u003eMichalakis K, Ilias I. COVID-19 and hyperglycemia/diabetes. World J Diabetes. 2021;12(5):642-50.\u003c/li\u003e\n \u003cli\u003eYang JK, Feng Y, Yuan MY, Yuan SY, Fu HJ, Wu BY, et al. Plasma glucose levels and diabetes are independent predictors for mortality and morbidity in patients with SARS. Diabet Med. 2006;23(6):623-8.\u003c/li\u003e\n \u003cli\u003eReiterer M, Rajan M, G\u0026oacute;mez-Banoy N, Lau JD, Gomez-Escobar LG, Gilani A, et al. Hyperglycemia in Acute COVID-19 is Characterized by Adipose Tissue Dysfunction and Insulin Resistance. medRxiv. 2021.\u003c/li\u003e\n \u003cli\u003eJuul S, Nielsen EE, Feinberg J, Siddiqui F, J\u0026oslash;rgensen CK, Barot E, et al. Interventions for treatment of COVID-19: Second edition of a living systematic review with meta-analyses and trial sequential analyses (The LIVING Project). PloS one. 2021;16(3):e0248132.\u003c/li\u003e\n \u003cli\u003eMueller AL, McNamara MS, Sinclair DA. Why does COVID-19 disproportionately affect older people? Aging. 2020;12(10):9959-81.\u003c/li\u003e\n \u003cli\u003eWHO. SURVEY TOOL AND GUIDANCE. 2020.\u003c/li\u003e\n \u003cli\u003eLe VT, Ha QH, Tran MT, Le NT, Le VT, Le MK. Hyperglycemia in Severe and Critical COVID-19 Patients: Risk Factors and Outcomes. Cureus. 2022;14(8):e27611.\u003c/li\u003e\n \u003cli\u003eAd\u0026rsquo;hiah AH, Al-Bayatee NT, Ahmed AA. Coronavirus disease 19 and risk of hyperglycemia among Iraqi patients. Egyptian Journal of Medical Human Genetics. 2021;22(1):82.\u003c/li\u003e\n \u003cli\u003eMohan M, Perry BI, Saravanan P, Singh SP. COVID-19 in people with schizophrenia: potential mechanisms linking schizophrenia to poor prognosis. Frontiers in Psychiatry. 2021;12:666067.\u003c/li\u003e\n \u003cli\u003eShe ZG, Zhu L, Cheng X, Qin JJ, Zhang XJ, Cai J, et al. Association of Blood Glucose Control and Outcomes in Patients with COVID-19 and Pre-existing Type 2 Diabetes. Cell Metab. 2020;31(6):1068-77.e3.\u003c/li\u003e\n \u003cli\u003eZhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054-62.\u003c/li\u003e\n \u003cli\u003eMazori AY, Bass IR, Chan L, Mathews KS, Altman DR, Saha A, et al. Hyperglycemia is associated with increased mortality in critically ill patients with COVID-19. Endocrine Practice. 2021;27(2):95-100.\u003c/li\u003e\n \u003cli\u003eBelice T, Demir I. The gender differences as a risk factor in diabetic patients with COVID-19. Iranian journal of microbiology. 2020;12(6):625-8.\u003c/li\u003e\n \u003cli\u003eMehraeen E, Karimi A, Barzegary A, Vahedi F, Afsahi AM, Dadras O, et al. Predictors of mortality in patients with COVID-19\u0026ndash;a systematic review. European journal of integrative medicine. 2020;40:101226.\u003c/li\u003e\n \u003cli\u003eSingh AK, Gupta R, Ghosh A, Misra A. Diabetes in COVID-19: Prevalence, pathophysiology, prognosis and practical considerations. Diabetes Metab Syndr. 2020;14(4):303-10.\u003c/li\u003e\n \u003cli\u003eJung C, Wernly B, Fj\u0026oslash;lner J, Bruno RR, Dudzinski D, Artigas A, et al. Steroid use in elderly critically ill COVID-19 patients. European respiratory journal. 2021;58(4).\u003c/li\u003e\n \u003cli\u003eSachdeva S, Desai R, Gupta U, Prakash A, Jain A, Aggarwal A. Admission hyperglycemia in non-diabetics predicts mortality and disease severity in COVID-19: a pooled analysis and meta-summary of literature. SN comprehensive clinical medicine. 2020;2:2161-6.\u003c/li\u003e\n \u003cli\u003eWu J, Huang J, Zhu G, Wang Q, Lv Q, Huang Y, et al. Elevation of blood glucose level predicts worse outcomes in hospitalized patients with COVID-19: a retrospective cohort study. BMJ Open Diabetes Research and Care. 2020;8(1):e001476.\u003c/li\u003e\n \u003cli\u003eSharma P, Behl T, Sharma N, Singh S, Grewal AS, Albarrati A, et al. COVID-19 and diabetes: Association intensify risk factors for morbidity and mortality. Biomed Pharmacother. 2022;151:113089.\u003c/li\u003e\n \u003cli\u003eCeriello A. Hyperglycemia and COVID-19: What was known and what is really new? Diabetes research and clinical practice. 2020;167:108383.\u003c/li\u003e\n \u003cli\u003eCariou B, Hadjadj S, Wargny M, Pichelin M, Al-Salameh A, Allix I, et al. Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: the CORONADO study. Diabetologia. 2020;63(8):1500-15.\u003c/li\u003e\n \u003cli\u003eSardu C, D\u0026apos;Onofrio N, Balestrieri ML, Barbieri M, Rizzo MR, Messina V, et al. Outcomes in Patients With Hyperglycemia Affected by COVID-19: Can We Do More on Glycemic Control? Diabetes care. 2020;43(7):1408-15.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Prevalence, Clinical characteristics, Hyperglycaemia, Diabetes Mellitus, COVID-19, Eastern Uganda","lastPublishedDoi":"10.21203/rs.3.rs-9012176/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9012176/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eHyperglycemia\u003cstrong\u003e \u003c/strong\u003eis one of the common complications in COVID-19 patients. Globally, hyperglycemia associated with COVID-19 was estimated to be 25%. Hyperglycemia results in increased morbidity and mortality yet proper screening and management protocols in developing countries. There is paucity of data in developing countries.\u003c/p\u003e\n\u003cp\u003eObjectives: To determine the prevalence of hyperglycemia, clinical characteristics, and outcomes of COVID-19 admitted patients in Mbale and Soroti regional referral hospitals - Uganda.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003e Retrospective cross-sectional study was conducted on adult admitted COVID-19 patient’s case files at two tertiary hospitals, in Eastern Uganda. 711 COVID-19 patient files with a capillary blood glucose test result during the study period from 1st March 2020 to 31st December 2021 were reviewed. Data was abstracted into a data collection tool specifically designed for this study. The variables included socio-demographics, clinical characteristics, and outcome status of the patients. Hyperglycaemia was defined based on the COVID-19 management algorithm as capillary blood glucose readings \u0026gt;11.1 mmol/l at or during admission, with the aid of the Glucometer One-Touch©. A primary outcome was hyperglycaemia in hospitalised patients. The Chi-Squared test was used for bivariate analysis, while the logistic regression model was applied for multivariate analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOverall, hyperglycaemia was detected in 459 out of 711 (65%) patients. Living in rural areas (AOR 1.7, 95% CI: 1.1-2.7, P \u0026lt; 0.027), having a medical history of diabetes mellitus (AOR 4.8, 95% CI: 3.4-6.7, P \u0026lt; 0.001), and current use of steroids (AOR 2.9, 95% CI: 1.8-4.7, P \u0026lt; 0.001) immediately before admission were statistically significantly associated with hyperglycemia in COVID-19 patients. \u0026nbsp;COVID-19 found to be an independent risk factor for Hyperglycaemia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe prevalence of hyperglycaemia among COVID-19 patients in eastern Uganda during the global epidemic was high, at 65%. Pre-admission conditions associated with hyperglycaemia included a medical history of diabetes mellitus, steroid use, living in rural area. \u0026nbsp;Strengthening screening for hyperglycemia and specific management protocols during epidemics and pandemics is recommended\u003c/p\u003e","manuscriptTitle":"Covid-19 a Strong Predictor of Hyperglycaemia Among Ugandan Patients: A Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 06:04:20","doi":"10.21203/rs.3.rs-9012176/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-16T07:20:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241088384283122507976999265229146108018","date":"2026-04-23T11:45:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202282742570004994686546834947590389766","date":"2026-04-21T10:20:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T09:45:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-13T11:13:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-09T08:25:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-07T11:10:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2026-03-07T11:05:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"480b24da-5dca-462a-a5d2-c1e54d4cdd8f","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-16T07:20:25+00:00","index":68,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T06:04:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 06:04:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9012176","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9012176","identity":"rs-9012176","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}