Prevalence and Associated Factors of Anemia in Diabetic Patients

Abstract

Introduction: Diabetes presents a significant global health challenge due to its association with a range of pathological changes, such as metabolic, cellular, and blood disturbances, that lead to long-term microvascular and macrovascular complications. Healthcare providers often underestimate anemia as a co-morbidity in diabetes, despite the wide range of prevalence estimates reported in the literature. Addressing anemia in diabetes patients early can reduce the incidence of these complications, as has been demonstrated.

Aim of the study: The study aimed to estimate the prevalence of anemia and its associated factors among patients with type 2 diabetes mellitus (T2DM).

Methods: This is a cross-sectional prospective study with a retrospective review of diabetic patient medical records attending family medicine clinics at four military hospitals (King Hussien Medical Center, Prince Rashed Military Hospital, Queen Aliah Military Hospital, and Prince Hashem Military Hospital) at Royal Medical Services. Adult patients over the age of 18 diagnosed with type 2 diabetes mellitus for more than one year will be included in the study. However, those patients with diseases (such as thalassemia and leukemia) or other systemic disorders (such as infectious diseases) that could result in anemia, those with acute conditions such as acute hemorrhage, those who received blood transfusions in the last three months, pregnant women, or type 1 diabetes will be excluded. 

Anemia will be defined as hemoglobin level < 13 g/dl in men and 12g/dl in females (Harrison TR, 2011). Type 2 DM will be defined glycated hemoglobin (HbA1c > 6.5%), fasting blood glucose (FBG > 126 mg/dl), random blood glucose (RBG > 200 mg/dl) (American Diabetes Association, 2012).

Patients’ medical records will be reviewed for patient medications, duration of diabetes, diabetic complications, and glycemic control. Furthermore, to exclude any patients who have one or more of the aforementioned conditions reported in the exclusion criteria. Meanwhile, researcher will collect the following data in the clinic after patients’ agreement to participate in the study which include: age, sex, marital status, smoking status, comorbidities, anthropometric measurements including weight, height and body mass index (BMI). Moreover, patient will ask to withdraw complete blood count (CBC) blood sample which include hemoglobin, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC) for diagnosing anemia if its present.

Patients will be divided into two groups based on the presence of anemia; anemic group and non-anemic group. Regarding study variables. Glycemic control will be classified into two categories based on HbA1c result (controlled diabetic group comprised those whose HbA1c level was equal to or <7.5% and poorly controlled diabetic group comprised those whose HbA1c level was >7.5%). Body mass index will classify according to Center of Disease Control (CDC) as follow: BMI score < 18.5 Kg/m² is underweight, 18.5–24.9 Kg/m² is normal, 25–29.9 Kg/m² is overweight and BMI ≥ 30 Kg/m² is obese.    

The sample size will be calculated based on Cohran’s sample size formula as: n=Z² pq /e². Here, p will be the anticipation of anemia prevalence in the diabetic population, q=1 - p; e is an acceptable error (5%); and Z= 1.96. A simple random sampling (systematic procedure) will be used to choose patients with type 2 diabetes in order to reduce the possibility of selection bias. With this technique, we randomly choose the first subject and then select the next subjects in a periodic manner according to K (interval). K will be determined based on the sampling frame prepared by researchers divided by n. 

Descriptive analysis will be used with the mean (standard deviation) for normally distributed variables and the median (inter-quartile range) for highly skewed distributions. The t-test and the Mann-Whitney test will be used to compare the baseline characteristics of quantitative variables between anemic and non-anemic groups for variables with a normal and highly skewed distribution, respectively. Categorical variables will be compared between the groups using the chi-square test. Logistic regression analyses will be employed to find out how these variables are associated with anemia. The results will be given as odds ratios (ORs) with a 95% confidence interval (CI). We will select only covariates with a p-value <0.20 in the univariate analysis to enter the logistic analysis.