Glycemic Control of Diabetes Mellitus Patients in Referral Hospitals of Amhara Region, Ethiopia: A Cross-Sectional Study

Risk factors for suboptimal glycemic control in pediatrics with type 1 diabetes mellitus: a cross-sectional study

The objective of this research is to analyze the influence of various factors on glycemic control in pediatrics with type 1 diabetes mellitus (T1DM). The study, a cross-sectional analysis, involved 221 T1DM patients below 18 years old who visited our clinic between 2011 and 2020, predating the COVID-19 outbreak. Out of the initial pool, 204 participants were chosen based on specific criteria. By computing odds ratios and 95% confidence intervals, we determined the correlation between these factors and achieving optimal glycemic control (HbA1c < 7.5%). Of the 204 individuals, 55.9% (113 patients) were female. The average age at diagnosis was 6.93 ± 3.9 years. Mean HbA1c (A1C) level of optimal and suboptimal groups were 6.97, 95% CI 6.84 to 7.1 and 8.86, 95% CI 8.68 to 9.03, respectively (p-value < 0.001). Fifty patients had optimal glycemic control and 154 people experienced suboptimal glycemic control during the follow-up that the prevalence of each of them was 24.51, 95% CI 18.7 to 31 and 75.49, 95% CI 68.99 to 81.22, respectively. In the assessment of risk factors associated with suboptimal glycemic control, patients aged 10-14 years had the highest likelihood of experiencing suboptimal glycemic control (crude odds ratio [COR] 3.12, 95% CI 1.04 to 9.3), followed by duration of diabetes (COR 2.85, 95% CI 1.2 to 6.8), which both were significant. By utilizing multivariable logistic regression analysis, a noteworthy finding emerged. It was revealed that patients aged 10-14 years exhibited a significant association with suboptimal glycemic control, [adjusted odds ratio (AOR) 4.85, 95% CI 1.32 to 17.7]. Additionally, a statistically significant correlation was identified between individuals with a body mass index (BMI) falling within the ≥ 95th percentile category and suboptimal glycemic control, Cramer's V = 0.21, p-value = 0.01. Our research has revealed a significant correlation between patients aged 10-14 years and obese individuals (BMI ≥ 95th) with suboptimal glycemic control. It is crucial to consider these factors as they can offer valuable insights during diagnosis, highlighting the increased risk of long-term suboptimal glycemic control.

Frequency and correlates of poor glycemic control in patients with type 2 diabetes at Jimma Medical Centre, Ethiopia: a cross-sectional study

Introduction

the majority of studies in Ethiopia determine the prevalence of glycemic control employed by fasting blood sugar (FBS), which is impacted by a variety of factors. Hence, the purpose of this study was to assess the status of glycemic control using HbA1c and its correlates in patients with type 2 diabetes in Southwest Ethiopia.

Methods

a cross-sectional study was employed among 124 T2 diabetes mellitus (DM) patients at Jimma Medical Center (JMC), Southwest Ethiopia. HbA1c and FBS were estimated using the Cobas 6000 analyzer. The body mass index (BMI) and waist-to-hip ratio were calculated as the standard formula. Data were analyzed by SPSS version 25. Logistic regression analysis was employed to identify independent risk factors associated with poor glycemic control of DM patients.

Results

males comprised 63.7% (n=79) of the total respondents. The mean age of aOR: 2.21, 95% CI 1.13, 4.34; p = 0.01f participants was 51.84 ± 11.6 years; 60.5% (n=75) of T2 DM patients were in poor glycemic control (HbA1c ≥ 7%). In multivariate logistic regression analysis, BMI of ≥ 30, (aOR: 2.21, 95% CI 1.13, 4.34) increased waist-to-hip ratio (aOR: 1.63, 95% CI 0.82, 2.18), high systolic blood pressure (aOR: 1.52, 95% CI 1.11, 6.23), high FBS (aOR: 1.61, 95% CI 1.00, 4.12), and longer duration of DM (aOR: 1.23, 95% CI 0.87, 1.88) were associated with poor glycemic control.

Conclusion

the level of poor glycemic control in the study population is high. Obesity and/or overweight, central obesity, systolic hypertension, and fasting blood sugar levels were all associated with poor glycemic control in T2 DM patients.

Prevalence of Glycemic Control and Factors Associated With Poor Glycemic Control: A Systematic Review and Meta-analysis

The primary method for preventing health and health-related problems in diabetic people is glycemic control. Numerous studies have documented the importance of glycemic control and the factors that influence it. However, the results are dispersed. This study sought to identify the prevalence of poor glycemic control and associated factors in Ethiopia. Findings will be crucial to reduce the burden of poor glycemic control. Comprehensive search was performed from databases: PubMed, Global Health, CINAHL, IRIS, and FSTA. Gray literature sources were also explored. This document was prepared based on the PRISMA flowchart. The protocol is registered in PROSPERO (ID: CRD42022296583). STATA software was used for data analyses and I2 test with Cochrane Q statistics, and forest plot were used to verify heterogeneity. When the I2 value was higher than 50% and the p-value was 0.05, heterogeneity was deemed significant. To confirm publication bias, a funnel plot with an Egger's regression test was performed. The factors related to poor glycemic control were reported using pooled odds ratios (POR) and a 95% confidence interval. In this study, 46 papers totaling 15 457 people were used to calculate the pooled estimates. It was determined that 32.6% (95% CI: 28.3, 36.9) and 61.92% (95% CI: 57.92, 65.92) of the subjects, respectively, had good and poor glycemic control. Being male protected against poor glycemic control, while poor diet adherence, lack of exercise, poor adherence to anti-diabetic medications, and smoking increased the likelihood of poor glycemic control. Lack of formal education, no family history of diabetes, lack of glucometer for blood glucose monitoring, comorbidity, being overweight and prolonged length of stay with diabetes all contributed to poor glycemic control. Ethiopia had a fairly high rate of poor glycemic control. The main determinants are lifestyle factors. Therefore, it is important to focus on behavioral interventions.

Determinants of hypertension among diabetes patients attending selected comprehensive specialized hospitals of the Amhara Region, Ethiopia: An unmatched case-control study

BACKGROUND

The coexistence of diabetes mellitus and hypertension is a worldwide public health problem causing significant morbidity, mortality, and decreased quality of life. Despite the increasing burden of hypertension among patients with DM, data on determinants of hypertension among patients with DM in the Amhara region of Ethiopia is scarce. Hence, this study identified determinants of hypertension among people with diabetes attending chronic disease follow-up clinics in the Amhara region of comprehensive specialized hospitals in Ethiopia.

METHOD AND MATERIALS

An institutional-based unmatched case-control study was conducted among 470 individuals with diabetes in the Amhara region's comprehensive specialized hospitals (Debre Berhan, Felege Hiwot, and Dessie Comprehensive specialized hospital). A multistage sampling technique was used to select participants for this study. We collected the data using standard questionnaires (short form of international physical activity questionnaire, Morisky medication adherence scale, patient health questionnaire, perceived dietary adherence scale, Oslo social support questionnaire, and alcohol use disorder identification test), physical measurements, and data extraction checklists. A multivariable binary logistic regression was fitted to identify determinants of hypertension, and we presented the findings using an adjusted odds ratio (AOR) with a 95% confidence interval (CI).

RESULTS

235 cases and 235 controls participated in this study. The median (IQR) age for the cases was 60 (52-66 = 14), and the mean age (± SD) for the controls was 51.72 (± 12.51). The significant determinants of hypertension with AOR [95% CI] were a lower level of physical activity: 1.82 [1.00, 3.31], depression: 2.00 [1.24, 3.21], family history of hypertension: 2.13 [1.34, 3.37], not having diabetic health education: 1.87 [1.18, 2.96], a longer duration of diabetes: 1.99 [1.05, 3.79], and poor glycemic control: 1.57 [1.01, 2.45].

CONCLUSION

In this study, determinants that increase the risk of hypertension among people with diabetes mellitus were older age, physical inactivity, depression, family history of hypertension, not having diabetic health education, a longer duration of diabetes, and poor glycemic control.

Physical activity can improve diabetes patients' glucose control; A systematic review and meta-analysis

Background

Glycemic control is vital to patient care, and it is still the most important treatment goal for reducing organ damage and other complications associated with diabetes. Physical activity is one of the factors that affects glycemic management. Therefore, the aim of this systematic review and meta-analysis was to find, evaluate, and synthesize the best available information on the link between physical exercise and glycemic control in Ethiopian diabetes patients.

Methods

Pubmed, Science Direct, Google Scholar and African Journals Online were the databases searched. In addition, gray literature were explored. All papers chosen for inclusion in the review underwent a thorough critical appraisal utilizing the Joanna Briggs Institute's standardized critical appraisal instruments (JBI critical appraisal checklist-2017). For statistical analysis and descriptive synthesis, quantitative articles were combined. The Odds ratio and their 95% confidence intervals were generated. Papers that were of excellent quality but lacked the main outcome (physical activity) for meta-analysis were subjected to descriptive synthesis.

Results

The finding of this meta-analysis showed diabetes patients who were physically active had controlled their blood glucose levels by 2.4 times compared to their counter (Odds ratio = 2.40, 95% Confidence Interval = 1.57,3.69). The duration of disease was found to be the most commonly reported predictor for poor glycemic control followed by dietary habits, patients' sex and age.

Conclusion

Physical activity, which is a simple and inexpensive therapy for diabetes patients, can help them control their blood glucose levels. Patients with diabetes who have had it for a long time should be aware of the need of regular physical activity in maintaining blood glucose control.

Assessment of magnitude and spectrum of cardiovascular disease admissions and outcomes in Saint Paul Hospital Millennium Medical College, Addis Ababa: A retrospective study

BACKGROUND

Cardiovascular diseases(CVD) remain the leading cause of death in the world and over 80% of all cardiovascular-related deaths occur in low and middle income countries. Ethiopia is in epidemiologic transition from predominantly infectious diseases to non-communicable diseases and the CVD is a major public health challenge.

METHODS

The aim of this study was to assess the magnitude and spectrum of cardiovascular admission and its outcomes among medical patients admitted to both Medical Ward and ICU of St. Paul Teaching Hospital from 1st of Jan 2020 to 1st of Jan 2021.

RESULTS

Out of 1,165 annual medical admissions, the prevalence of cardiovascular diseases(CVD) was 30.3%. About 60%(212) of patients had advanced congestive heart failure of diverse causes. Hypertensive heart disease (HHD) was the next predominant diagnosis (41%(146)), and also the leading cause of cardiac diseases followed by rheumatic valvular heart disease(RVHD) (18%(64)) and Ischemic heart disease (IHD) (12.2%(43)), respectively. Yong age, rural residence and female sex were associated with RVHD(p = 0.001). Stroke also accounted for 20%(70) of CVD admission (hemorrhagic stroke-17% Vs Ischemic stroke-83%). Hypertension was the predominate risk factor for CVD and present in 46.7%(168) of patients. The mean hospital stay was 12days and in-hospital mortality rate was 24.3% with septic shock being the commonest immediate cause of death followed by fatal arrhythmia, brain herniation, and massive PTE.

CONCLUSION

Cardiovascular diseases were common in the study area causing significant morbidity and mortality. Therefore, comprehensive approach is imperative to timely screen for cardiovascular risk reduction, disease control and complication prevention. Strategies should also be designed to increase public awareness regarding the cardiovascular risk reduction, drug adherence, and possible complications.

Prevalence and Factors Associated with Diabetic Retinopathy among Adult Diabetes Patients in Southeast Ethiopia: A Hospital-Based Cross-Sectional Study

BACKGROUND

Diabetic retinopathy (DR) is the most prevalent microvascular consequence of diabetes mellitus, and it can result in blindness that is irreversible. Due to delayed diagnosis and limited access to diabetic care, the situation is even worse in developing countries. Scientific evidence on the prevalence of DR and its associated factors among diabetes patients in low-income countries, such as Ethiopia, is limited. This study aimed to determine the prevalence of DR and associated factors among adult diabetes patients in southeast Ethiopia.

METHODS

A hospital-based cross-sectional study was conducted among diabetes patients who visited Madda Walabu University Goba Referral Hospital. Fundus and slit-lamp examination were performed for screening of DR. Multivariate binary logistic regression was computed to identify factors associated with DR.

RESULTS

A total of 256 patients (144 men, 56.2%) aged 50.15±15.71 years were included in the study. The prevalence of any DR was 19.9% (95% CI 15.4%-25.3%), mild nonproliferative diabetic retinopathy (NPDR) 10.9% (95% CI 7.6%-15.4%), moderate NPDR 5.9% (95% CI 3.5%-9.5%), severe NPDR 0.9% (95% CI 0.2%-3.9%), and proliferative DR 2.3% (95% CI 1.0%-5.1%). Duration of diabetes ≥10 years (AOR 10.22, 95% CI 1.70-61.44), central obesity (AOR 5.42, 95% CI 1.38-21.19), overweight/obese (AOR 2.65, 95% CI 1.02-6.92), lower high-density lipoprotein (HDL) cholesterol (AOR 5.82, 95% CI 1.86-18.24), moderate triglyceride:HDL cholesterol ratio (AOR 4.13, 95% CI 1.13-15.15), and urban dwelling (AOR 2.84, 95% CI 1.04-7.78) were significantly associated with DR.

CONCLUSION

One in every five DM patients had DR. Sociodemographic, anthropometric, and blood lipids were independently associated with DR. To reduce the burden of diabetes, strategies that focus on lifestyle modifications targeted at identified modifiable risk factors are essential.

Poor glycemic control and associated factors among pediatric diabetes mellitus patients in northwest Ethiopia, 2020: facility-based cross sectional retrospective study design

Diabetes mellitus is a global public health problem. Glycemic control is a major public health problem. Diabetes results from elevated levels of glycaemia such as increased glucose and glycated hemoglobin, and controlling glycaemia is an integral component of the management of diabetes. Glycemic control in children is particularly difficult to achieve. Identifying determinants of poor glycemic control is important for early modification of diabetic related end organ damages. This study was aimed to assess the status of glycemic control and associated factors among pediatric diabetes mellitus patients in northwest Ethiopia. Facility-based cross sectional retrospective cohort study design was used and this study was conducted from September, 2015 to February, 2018. Simple random sampling was used to select 389 samples. Data were collected using an extraction checklist. Data were entered into Epi-data − 4.6, and analyzed using Stata-16. Finally, multivariable binary logistic regression was done. Poor glycemic control was more common among pediatric patients 39.3% (95% CI 34.6, 44.3). Treatment discontinuation (AOR 2.42, 95% CI 1.25, 4.69), age (AOR 1.15, 95% CI 1.03, 1.28) and treatment dose (AOR 0.96, 95 CI 0.92, 0.99) were significantly associated with poor glycemic control. Prevalence of poor glycemic control was high. Patient’s age, history of treatment discontinuation and dose of treatment were the significant contributing factors to poor glycemic control. These need to be addressed to attain the objective of adequate glycemic control.

Glycemic control and its associated factors in type 2 diabetes patients at Felege Hiwot and Debre Markos Referral Hospitals

Poor glycemic control is a main public health problem among type 2 diabetes mellitus (T2DM) patients and a significant cause of the development of diabetic complications. This study aimed to assess the glycemic control status and its associated factors among type 2 diabetes patients in Felege-Hiwot and Debre Markos Referral Hospitals. A retrospective cohort study was conducted at Felege-Hiwot and Debre Markos Referral Hospitals from December 2014 to December 2015. We have reviewed the chart of these patients until January 2020. Type 2 diabetic patients on follow-up at Felege-Hiwot and Debre Markos Referral Hospitals who fulfilled the inclusion criteria of the study were included. The primary outcome was the level of blood glucose during the study period. Good glycemic control was defined as patients whose average fasting blood glucose measurement for three consecutive visits was between 70 and 130 mg/dL. A generalized linear mixed autoregressive order one model was used to identify the determinants of glycemic control. A total of 191 patients with 1740 observations were included in the study. The overall prevalence of good glycemic control was 58.4% (95% CI: 57.159.7%). The factors associated with good glycemic control at 95% confidence level adjusted odds ratio were being residing in rural (CI: 0.454, 0.614), negative proteinuria (CI: 1.211, 1.546), diastolic blood pressure < 90 (CI: 1.101, 1.522), systolic blood pressure < 140 (CI: 1.352, 1.895), serum creatinine (CI: 0.415, 0.660), duration per visit (CI: 0.913, 0.987), duration since diagnosis (CI: 0.985, 0.998), weight ≥ 78 kg (CI: 0.603, 0.881). Age 38–50, 51–59 and 60–66 years (CI: 1.267, 1.776), (CI: 1.057, 1.476) and (CI: 1.004, 1.403), respectively. The overall prevalence of poor glycemic control was high at Debre Markos and Felege Hiwot Referral Hospital. Living in a rural area, older age (≥ 67 years), positive proteinuria, higher weight (≥ 78 kg), higher serum creatinine levels, higher duration per visit, higher time duration of T2DM since diagnosis, and developing hypertension (SBP ≥ 140, DBP ≥ 90) were the predictors of lower good glycemic control achievements of T2DM patients. In response to this finding, an aggressive intervention that targets improving glycemic control is required.

Predictors of blood glucose change and microvascular complications of type 2 diabetes mellitus patients in Felege Hiwot and Debre Markos referral hospital, North West Ethiopia

BACKGROUND

Microvascular complications lead to disability, dependency, and accelerated morbidity and mortality. This study aimed to identify predictors of blood glucose change and time to microvascular complications among patients with type 2 diabetes.

METHODS

A retrospective cohort study was conducted among type 2 diabetes mellitus patients enrolled between December 2014 and December 2015 at Felege Hiwot and Debre Markos Referral Hospital. A total of 318 T2DM patients were included in the study. Joint modelling of longitudinal and survival analysis was employed to identify predictors of Blood Glucose Change and Microvascular Complications in Type 2 Diabetes Mellitus Patients.

RESULTS

The prevalence of microvascular complications in Type 2 diabetes patients was 26.3%, 95%confidence interval(CI):(21.5, 31.1). Of which, half of the patients developed a microvascular complication after 30 months from the onset of the follow-up. The significant predictors of developing microvascular complication were positive proteinurea (adjusted hazard ratio (AHR) = 1.418, 95%CI: 1.080, 1.861), Serum creatinine (AHR = 3.704, 95%CI: 1.992, 6.887), Weight (AHR = 1.058, 95%CI: 1.023, 1.094), and log fasting blood glucose(log(FBS))(AHR = 1.013, 95%CI: 1.010, 1.015). The predictors of fasting blood glucose progression were higher baseline FBS(est(estimate) = 0.002,95%CI:0.0018, 0.0022), Systolic blood pressure (SBP) (est = 0.003, 95%CI: 0.002, 0.004), diastolic blood pressure (DBP) (est = 0.002, 95%CI: 0.0002, 0.004), and age (est = 0.003, 95%CI: 0.001, 0.004).

CONCLUSION

The progression of the fasting blood glucose level for rural patients was faster than for urban patients. Patients having higher baseline FBS, previous hypertension history, higher SBP, higher DBP, older age, and fewer visits to the hospital have a relatively more progressive change in blood sugar levels. Patients having higher triglyceride levels, positive proteinuria, higher fasting blood sugar, higher weight, and a lesser number of hospital visits have a higher risk of developing a complication. In response to this finding, an aggressive intervention that targets to prevent microvascular complications is required.

Evaluation of glycemic control and related factors among outpatients with type 2 diabetes at Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia: a cross-sectional study

BACKGROUND

The goals of glycemic management for patients with diabetes are to prevent or delay complications and optimize quality of life. However, in clinical practice, the recommended glycemic control target is difficult to achieve. Therefore, it is important to identify factors that influence the outcomes of glycemia to improve the quality of diabetic management. The study aimed to evaluate the level and factors associated with glycemic control among type 2 diabetic outpatients at Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia.

METHODS

A hospital-based cross-sectional study was conducted among systematically selected 325 patients with type 2 diabetes who attended diabetic clinics at Tikur Anbessa Specialized Hospital. Pretested, structured, and interviewer-administered questionnaires were used to collect sociodemographic and diabetes-related information from March 1 to May 30, 2021. HbA1c was used to assess glycemic control according to the HbA1c target of < 7% ('good' control) as recommended by the American Diabetes Association for non-pregnant adults. The HbA1c level in the range of 7-8% was defined as 'inadequate' control and 'poor' at levels > 8%. Data entry and analysis were performed using SPSS v26. Multivariate logistic regression analysis was used to identify determinants of glycemic control.

RESULTS

The median level of HbA1c of the participants was 8.4% (IQR 6.8-10.1). And approximately three-quarters (73.8%) of the patients had inadequate and poor glycemic control (HbA1c ≥ 7%). Older age (AOR: 2.46, 95% CI: 1.28-6.01), DM duration of > 10 years (AOR: 3.15, 95% CI: 2.22-6.54), insulin therapy (AOR: 3.07, 95% CI: 2.10-6.12), poor diet compliance (AOR: 1.97, 95% CI: 1.28-3.52) and failure to set goals for glycemic control (AOR: 3.42, 95% CI: 2.17-5.97) were factors associated with inadequate and poor glycemic control.

CONCLUSIONS

The study revealed that a significant number of diabetic patients had inadequate and poor glycemic control levels. And this was associated with older age, longer duration of DM, insulin therapy, poor diet compliance, and failure to set control goals. This requires a focus on the associated factors identified and tailored management mechanisms to maintain good glycemic control.

Poor Glycemic Control and Its Contributing Factors Among Type 2 Diabetes Patients at Adama Hospital Medical College in East Ethiopia

BACKGROUND

Poor glycemic control is a major public health issue among patients with type 2 diabetes mellitus and a significant risk factor for the progression of diabetic complications. This study aimed to assess the magnitude and contributing factors of poor glycemic control among type 2 diabetes patients on follow-up at Adama Hospital Medical College (AHMC) in East Ethiopia.

METHODS

A cross-sectional study was conducted among 245 type 2 diabetes patients on follow-up at AHMC from March 1 to 30, 2020. All type 2 diabetes patients on follow-up at AHMC and fulfilling the inclusion criteria were included in the study. The data were collected through face-to-face interviews using structured questionnaires and reviewing the patient chart. The data were entered and analyzed using SPSS version 21. The level of significance was declared at a p-value of <0.05.

RESULTS

Of the 245 type 2 diabetes patients included in the study, 157 (64.1%) had poor glycemic control. The factors associated with poor glycemic control were being male (AOR = 2.28, 95% CI [1.24-4.21]), not attending formal education (AOR = 3.12, 95% CI [1.53-6.35]), monthly income of <136 USD (AOR = 2.14, 95% CI [1.17-3.91]), overweight (AOR = 2.60, 95% CI [1.32-5.10]) and obesity (AOR = 3.44, 95% CI [1.44-8.21]), and chewing khat (AOR = 2.77, 95% CI [1.04-7.33]).

CONCLUSION

The proportion of poor glycemic control among type 2 diabetes patients at AHMC was remarkably high. Therefore, more effort should be taken to strengthening and disseminating health education programs for diabetes patients at each follow-up visit on the importance of achieving optimal body weight, avoiding khat chewing, and maintaining regular physical exercise to prevent and mitigate the complications resulting from poor glycemic control.