Diabetes mellitus (DM) is a commonly known pandemic non-communicable disease (NCD) affecting 1.6 million people and accounting for 4 million deaths [1-3]. It is a group of chronic metabolic disorders characterized by a high level of blood glucose due to a defect in insulin secretion, insulin action, or both [1-4]. A high level of glucose in DM patients causes vascular damage affecting the heart, eyes, kidneys, and nerves, which causes various complications [5,6] and it is associated with long-term microvascular (retinopathy, nephropathy, and neuropathy) and macrovascular (ischemic heart disease, stroke, and peripheral vascular disease) complications [7-9] Furthermore, approximately 673 billion US dollars have been spent globally on diabetic patient health expenditure [6,10]

Regardless of economic, developmental, epidemiological, or demographic diversity, DM is a burden on all nations [4,5,11]. Additionally, the prevalence of DM in adults has been rising in recent decades, which has resulted in premature deaths and a significant economic burden worldwide [1,3]. According to the International Diabetes Federation Atlas study from 2017, there are 451 million people (aged 18 to 99) who have DM worldwide, and that figure is projected to increase to 693 million by the year 2045. Moreover, the number of adults living with diabetes will further expand by 50% by 2030 in the world [2,6,12,13]. An increase in the prevalence of diabetes provides evidence that one out of every ten adults will have diabetes by 2035, but half of those with diabetes are not diagnosed [14,15].

Over the past few decades, the prevalence of DM has increased across Sub-Saharan Africa as a result of the effects of rapid urbanization, globalization, and lifestyle changes [16,17]. Africa is home to more than two-thirds of all DM cases worldwide.16, 17 Along with smoking habits, systolic blood pressure (SBP), diastolic blood pressure (DBP), waist-hip circumference (WHC), and total cholesterol, the prevalence of DM is also significantly correlated with factors like poor dietary practices, lack of physical activity, rising rates of overweight and obesity, excessive alcohol consumption, and rising rates of overweight and obesity [1,6,8,18-20].

Prediabetes mellitus (PDM) is a serious health condition where the blood sugar levels are higher than normal, but not high enough yet to be diagnosed as type 2 diabetes. It represents the intermediate stage of abnormal glucose metabolism that indicates the borderline of diabetes [3,4]. It has also similar risk to diabetes and the complications. According to current findings, to control diabetes at early stage, tackling the progression of prediabetes to diabetes is a business venture. PDM was highly prevalent among adolescents as reported in the previous report. By 2030, it is estimated that more than 470 million people will be at risk of developing prediabetes.5 Approximately, 25% of individuals with PDM have the chance to develop type 2 DM within 3 to 5 years.6 Now a day, screening of prediabetes is needed to identify vulnerable groups to DM and reduce the number of people with DM in the future.

In Sub-Saharan Africa, Ethiopia is one of the top five nations with the greatest DM prevalence. Ethiopia has a 3.2% prevalence of DM in 2015, according to the national WHO STEPS study, while the Ethiopian Diabetes Association estimated that 1.33 million Ethiopians have diabetes.15, 20 Additionally, independent studies have shown that the prevalence of DM in Ethiopia ranges from 0.5% to 6.5% [1,8,12,15,17] while a community-based study discovered that the prevalence of DM ranged from 2.1% to 12% [13,19]. On the other hand, according to estimates from 2014, Ethiopia has 1,603,100 people with undiagnosed diabetes mellitus, or 75.1% of the population [1,21,22]. However, there is currently no information on the prevalence of diabetes mellitus (DM) and the factors that contribute to it among the adult population at Arba Minch Demographic and Health Surveillance System (AM-HDSS) sites. As a result, the goal of this study was to determine the prevalence and contributing factors of DM among adults in AM-DHSS, Arba Minch, Southern Ethiopia.

Study Area, Period and Study Design

A community-based cross-sectional study was conducted from March 1^st to May 30^th, 2021 at AM-HDSS sites in southern Ethiopia. They are located near Arba Minch town, with a range distance of 11.5 km from Ganta Meyechie to 59 km from Zeyesie Dembele. Arba Minch, the capital city of Gamo Zone, is located 505 km south of Addis Ababa, the capital of Ethiopia. It is found at an altitude of 130 above sea level with an average temperature of 29 ⁰C. Based on the 2007 Census conducted by the Central Statistical Agency (CSA), these districts have a total population of 164,529, of whom 82,199 are men and 82,330 are women. According to the AM-HDSS report, there is a total population of 74,157 at the surveillance site.

Study Population and Eligibility Criteria

All adult participants who were residents of AM-DHSS during the study period, were > 18 years old, and had lived in the kebele for at least 6 months were included in the study population. Whereas, pregnancy, study participants with a history of drug usage in the previous three weeks (such as diuretics and steroids), and study participants with a history of major surgery in the past three months were excluded.

Sample Size Determination and Sampling Technique

The sample size was determined by the single population proportion formula, assuming a 12% proportion of DM in Koladiba town, northwest Ethiopia 19 at a 95% confidence interval, a 3% margin of error, and a 10% non-response rate. Accordingly, the calculated total sample size was 496. Then a sampling frame was created using lists of households from 9 kebeles obtained from the DHSS site's database. The sample size was proportionally allocated to each kebele as per the house number. A simple random sampling technique was applied to select households, and then, in households where there was more than one eligible person, one participant was recruited using the lottery method.

Data Collection Tools

The World Health Organization (WHO) stepwise approach for NCD surveillance was used to collect the data in three categories. Socio-demographic data including age, sex, gender, educational level, family history of DM, and marital status and behavioral characteristics including chat chewing, smoking, alcohol consumption, fruit and vegetable consumption, physical activity, and family history of DM were collected through face-to-face interviews using a pretested structured questionnaire.

Anthropometric and Biochemical Measurements

Following the interview, participants were instructed to fast for at least 8 hours overnight. The next morning, fasting blood glucose (FBG) levels were measured using a glucometer (Diabetes Care Plan @ Home at Rs 1199) and diabetics were defined as >126 mg/dl. Anthropometric parameters were measured using standardized techniques and calibrated equipment. Weight was measured to the nearest 0.1 kilograms using a person scale when the participants were wearing light clothes and bare feet. Height was measured by a stadiometer at a height of 0.01 meters when the participants were in an upright standing position on a flat surface without shoes. The body mass index (BMI) was calculated by dividing the weight in kilograms by the height in meters squared (BMI = weight/ (height) ²). Obesity, overweight, normal weight, and underweight were generally defined as having a BMI of 30 kg/m² or higher, 25–30 kg/m², 18.5-24.9 kg/m², and less than 18.5 kg/m², whereas waist-hip circumference (WHC) was measured by elastic-plastic tape at the approximate center between the lower margin of the last palpable rib and the top of the iliac crest, and values of > 94 cm and > 80 cm for men.

Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured in a sitting position on the right arm using an electronic blood pressure monitor (Jiangsu Folee Medical Equipment CO, LTD). The three measurements were taken every 21 minutes, and the mean value of the three measurements was taken as the final reading of BP. SBP of 140 mmHg and/or diastolic blood pressure of 90 mmHg was defined as systolic hypertension. Physical activity was measured by using the WHO physical activity questionnaire. The questionnaire assessed work-related activity, walking, sports and recreational activity, and time spent sitting per day. Participants who were considered vigorous were those who fulfilled the recommendations for vigorous physical activity ≥3 days per week for ≥20 min per session [1,23].

Data Quality Assurance

The quality of the data was assured through the use of a pretested structured questionnaire on 5% of the total sample size, providing data collector training, and the active involvement of the supervisors in the data collection process. Nearly every day, the questionnaire was reviewed for consistency, accuracy, clarity, and completeness. To reduce observer bias in the measurement and recording, physical measures were made twice, and in some cases, three times. In addition, the blood glucose, blood pressure, and weight scale instruments were checked daily for accuracy against a standard calibrated instrument.

Data Processing and Analysis

Data processing and analysis were done using the Statistical Package for the Social Sciences, version 25. Data were summarized as frequencies, prevalence and mean using descriptive statistics. Logistic regression analysis was applied to identify predictor variables associated with DM. The Hosmer and Lemeshow chi-square goodness-of-fit test was used to check the fitness of the model. Bivariate logistic regression analysis was used to assess associated factors. Variables with a p-value less than 0.25 in the bivariable analysis were jointly entered into a multivariable analysis. The presence of associations and statistical significance was determined at a p-value less than or equal to 0.05.

Ethical Clearance

The study was approved by the Institutional Review Board of Arba Minch University, College of Medicine and Health Sciences (Ref. No. IRB/1048/21). Furthermore, support letters were obtained from the concerned bodies in each kebele of the AM-HDSS site. Each study participant gave their verbal agreement in accordance with the Declaration of Helsinki—Ethical principles for medical research involving human subjects before the delivery of the questionnaires, physical examinations, and blood sample collection. All conventional safety measures were taken to prevent COVID-19. All the information obtained from the study subjects was coded to be maintained confidentially. Any participant who was diagnosed with DM was linked to the nearest health facility for further diagnosis and treatment follow-up.

Socio-Demographic Characteristics of the Study Participants

A total of 496 study participants were recruited from nine selected kebeles of HDSS sites for this study. Of the total participants, 56.5% (n = 280) were males and 50.6% were aged above 35 years old. The age of the participants ranges from 18–82 years, with a median (IQR) of 36 years. More than two-thirds (89.1%) of the participants were married, and 41.7% of the participants were farmers. About 34.1% of study participants had a monthly income of 600-1500 Ethiopian birr, whereas 47.4% of participants had more than five family members (Table 1).

The overall prevalence of diabetic mellitus (DM) was 6.5% (32/496). The prevalence was higher among males (53.1%) than females (46.9%). Participants aged over 35 years had the highest frequency of DM (68.8%). In terms of marital status, it was discovered that married people had the highest prevalence of DM (87.5%), whereas 50% of DM were identified among participants with a monthly income of 600-500 Ethiopian birr (Table 1).

Table 1: Prevalence of Diabetic Mellitus In Relation To Socio-Demographic Characteristics of Study Participants (N = 496) At AM-HDSS Sites, Southern Ethiopia, 2021.

The overall prevalence of DM was 6.5% (32/496) with a 95% CI of 4.5%–9.0%, which is comparable with the estimated prevalence of diabetes in Hosanna, Ethiopia, 5.7% [12], Mizan-Aman Ethiopia, 6.5% [8], Dessie Ethiopia, 6.8% [1], and Northwest, Ethiopia, 4.9% [24], and Ghana, 6.3% [25], but significantly higher than the findings of the WHO STEPs survey (3.2%) [26] and studies conducted in various parts of Ethiopia, ranging from 1.9% to 3.3% [4,13,15]. However, the prevalence of DM in this study was lower than in the previous studies in Ethiopia, which ranged from 10.2% to 12% [19,20,22,27], in Sudan, 10.0% [28], and in the Saudi community, 30% [29]. The difference may be due to the socio-economic conditions and general lifestyle variations of the study patients. On the other hand, the vast majority of diabetic (81.3%) participants in this study were unaware of their DM status, which is comparable with the proportion of undiagnosed DM in the previous study (88.5%) [8]. However, it is higher than the previous report in Addis Ababa, Ethiopia (68.1%) [30] and Northeast, Ethiopia (72.5%) [1]. The high rate of undiagnosed diabetes in the current study showed that a lack of DM awareness and poor screening program in the community.

The findings of multivariate analysis in this study showed that, advanced age greater or equal to 35 years has shown a positively and statistically significant association with DM, which is in line to previous research findings in Ethiopia and Bangladeshi[1,15,22,31]. There is evidence that aging and diabetes share common pathophysiological pathways, and that aging alone is associated with a decline in physiological function, including a gradual decrease in basal metabolic rate, stress-induced hypercortisolism, hypogonadism, decreased growth hormone secretion, concomitant insulin resistance, and abdominal fat deposition. However, the mechanisms linking advancing age to metabolic dysregulation are multifactorial and complex[32].

Participants with a monthly income of 600–1,500 Ethiopian birr were more likely to have diabetes than those in the highest income groups. This is mostly due to the fact that low-income communities have been found to have greater rates of obesity, stress, and unhealthy behaviors like smoking and excessive drinking, all of which have been linked to an increased incidence of diabetes. Besides, Low-income communities confront particular difficulties with respect to lack of knowledge, access to medical services and drugs, and failure to manage diabetes efficiently and prevent complications [33].

This study also revealed that participants with mild physical activity experience increased their risk of acquiring diabetes, which was corroborated by earlier studies [27,28], showed that people who engaged in little to no physical activity were considerably more likely to develop diabetes. In fact, a lack of physical activity, which can result in fat storage and the release of free fatty acids from adipose tissue, as well as a reduction in the muscle and liver's sensitivity to insulin, can affect the body's metabolism. This can increase glucose levels, insulin resistance, and the risk of developing diabetes [28]. Similarly, FHDM was also found to be statistically associated with DM, which is in line with previous studies in Dessie and North West Ethiopia [1]. The primary cause of this is that genetic disorders may significantly contribute to the onset of diabetes. Though it is uncertain if DM is primarily brought on by genetic predisposition, the disease has a hereditary propensity and may also in relation to behavior such as living habit and physical inactivity experiences [1,28].

Systolic prehypertension was also shown to be statistically associated with DM, which is in agreement with other studies [1,8,14,27,28]. This may be so because hyperinsulinemia and the risk of developing other insulin-resistant diseases like diabetes mellitus are both impacted by high blood pressure. This recurrence is partly caused by physiological characteristics, which suggests that the effects of one disease tend to make the occurrence of the other disease more likely. Furthermore, due to their shared risk factors, the two diseases are more likely to co-occur [35]. The co-occurrence increases the risk of development of macrovascular and microvascular complications, and it has been observed that in hypertensive diabetics, the risk of death due to cardiovascular disease is almost doubled [36].

Limitation of the Study

This study is limited by a cross-sectional study design and only adults over 18 years of age. Recall bias may also have an impact on some of the self-reported statistics. Besides, FBG was employed in this study as a DM diagnosis technique, which is not a specific test for diabetes and does not indicate the participants' prior health state such as amount of blood glucose attached to hemoglobin (HgbA1c).

The overall prevalence of DM among the adult population is comparable with other previously conducted studies in Ethiopia. Age greater or equal to 35 years, average monthly income (600–1500 in Ethiopian birr), lack of regular physical activity experience, a family history of DM, and systolic prehypertension were found to be statistically significant associations with DM. Furthermore, more than 80% of diabetic patients had not been screened and were unaware of their diabetic status. As a result, raising awareness about healthy behavior and implementing diabetes screening programs at the community level are required to reduce complications and unnecessary financial expenditure by providing early treatment.

Abbreviations

AM-HDSS: Arba Minch Demographic and Health Surveillance System, BMI: Body Mass Index, DBP: Diastolic Blood Pressure, DM: Diabetes Mellitus, FBG: Fasting Blood Glucose, FHDM: Family History of Diabetes Mellitus, NCD: Non-Communicable Disease, SBP: Systolic Blood Pressure, WHO: World Health Organization.

Acknowledgment

We would like to show gratitude to Arba Minch University Health Demographic Surveillance System Office for coordinating and budget and material support in the conducting of this project. We want to also extend our thankfulness to all study participants for their time and willingness.

Availability of Data

Due to confidentiality concerns, the data sets evaluated in this work are available from the principal author upon reasonable request.

Funding Statement

The project was supported by the Arba Minch University, College of Medicine and Health Sciences HDSS office.

Author Contributions

All authors made a significant contribution to the work reported, whether that is, in the conception, study design, execution, acquisition of data, analysis, and interpretation, or in all these areas; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Conflict of Interest

The authors declare that they have no conflicts of interest in this work.

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