Prevalence of Chronic Kidney Disease and its Associated Factors Among Patients With Diabetes Mellitus at Government Hospitals in Addis Ababa,Ethiopia

Abstract

Background

Chronic kidney disease (CKD) is the presence of an abnormality in kidney structure or function that persists for more than three months. The prevalence of CKD among diabetic patients in developing countries has not been well studied. Therefore, this study aimed to determine the prevalence of CKD and related factors among diabetes patients who visited particular Addis Ababa government hospitals.

Methods

An institution-based cross-sectional study was conducted from July 2023 to May 2024 among 304 people with diabetes. Data were collected using a questionnaire through face-to-face interviews. Binary logistic regression analyses were carried out to identify factors associated with CKD.

Results

The study findings showed that 35 (11.5%) had CKD. People living in urban area including cities and towns (AOR = 6.68; 95% CI (1.95–22.91), not engaged physical activity: It encompasses all movement, including exercise, sports, and even everyday activities like walking, cleaning, or working (AOR = 1.95; 95% CI (1.33–11.50)), body mass index (BMI) between 18 and 24.99 kg/m² (AOR = 2.05; 95% CI (1.38–3.05), duration of diabetes after diagnosis (10 to 14 years) and >14 years (AOR = 6.16; 95% CI (1.03–36.89)), as well as not correctly taking prescribed medication (AOR = 7.75; 95% CI(1.31–18.50), patients with co-morbidities (AOR = 3.0 (95%CI (1.0–8.6), and daily consumption of sugar and fat (AOR = 3.0 (95% CI(1.01–6.39)) were found to be significantly associated with these variables.

Conclusion

This study demonstrated that the proportion of CKD in the study participants was relatively high compared to previous studies. Multivariable logistic regression analysis showed that residence, physical activity, body mass index, duration of diabetes after diagnosis, status of taking prescribed medication, patients with co-morbidities, daily consumption of sugar and fat were significantly associated with CKD (p < .05).

Keywords

chronic kidney disease diabetes mellitus prevalence risk factors

Introduction

Chronic kidney disease (CKD) is one of the most prominent causes of death, affecting over 10% of the world's population (Kovesdy, 2022). The prevalence of CKD is particularly high among patients with diabetes mellitus (DM), with more than 40% of individuals with diabetes expected to develop CKD (Alemu et al., 2020). CKD remains a public health problem (Deng et al., 2021), which affects over 75 million people worldwide (Deng et al., 2021; Kovesdy, 2022). Diabetes and hypertension are identified as major risk factors for the development and progression of CKD, affecting nearly 5–7% of the world population (Damtie et al., 2018).

Review of Literature

A meta-analysis conducted in the Middle East reported that the prevalence of CKD in patients with DM ranged from 10.8% to 60.78% (Coll-de-Tuero et al., 2022). The study done in Thailand found that the prevalence of CKD among primary care Type 2 DM (T2DM) patients was 24.4% (Ahmed et al., 2022). A study conducted among T2DM patients at a primary care health facility in Spain reported that the prevalence of CKD was 34.6% (Hariparshad et al., 2023).

A systematic review conducted in Africa included 32 studies from 16 countries, revealed that the overall prevalence of CKD among people with diabetes varied widely, ranging from 11% to 83.7% (Kaze et al., 2018). A study done in South Africa reported that the prevalence of CKD among patients with DM was 26.0% (Noubiap et al., 2020). The study done in Ethiopia among type 2 diabetic patients reported that the cumulative incidence of CKD was 10.8% (Adem et al., 2024).

CKD is a significant health problem in Africa, particularly among patients with DM. However, only a limited number of studies have investigated its prevalence and associated factors in this population, including in Ethiopia. The available literature also reveals considerable variability in reported findings. Identifying risk factors for CKD among diabetes patients is essential for developing targeted preventive strategies. Therefore, this study seeks to determine the prevalence of CKD and identify its associated risk factors. The findings will provide evidence to guide the design of effective, evidence-based interventions.

Methods

Study Design, Setting, Source, Study Population, and Sampling Techniques

An institution-based cross-sectional study design was conducted from July 2023 to May 2024 in public hospitals of Addis Ababa, Ethiopia. Of the 13 public hospitals, the study was conducted at Menelik II Comprehensive Specialized Hospital, Yekatit 12 Comprehensive Specialized Hospital, Ras Desta Dametew Comprehensive Specialized Hospital, and Zewditu Memorial Comprehensive Specialized Hospital. All patients with DM who were receiving follow-up care at public hospitals in Addis Ababa, Ethiopia, constituted the source population for this study. The study population included all DM patients who attended follow-up clinics at the selected public hospitals during the data collection period. A total of 304 diabetic patients were included in the study. These participants were selected through a systematic sampling technique following proportional allocation across the selected hospitals. The sample selection process was carried out in several stages to ensure representativeness and minimize selection bias. In the first stage, four public hospitals were selected from the 13 governmental hospitals in Addis Ababa using a simple random sampling technique. This random selection helped ensure that each hospital had an equal chance of being included, thereby enhancing the generalizability of the findings to all public hospitals in the city.

In the second stage, a proportional allocation of the total sample size (n = 304) was made to each selected hospital based on the number of DM patients receiving follow-up care in that facility. This approach was used to maintain the correct weighting of each hospital in the overall sample, ensuring that hospitals with larger diabetic populations contributed proportionally more participants to the study.

In the final stage, participants were selected from each hospital using a systematic sampling technique. The sampling interval (k) was determined by dividing the total number of DM patients in the hospital's registry by the number of patients allocated to that hospital. The first participant was selected by simple random selection from the first k patients, and then every k^th patient was included until the required sample size was reached. This method was chosen because it is straightforward to implement in clinical settings and provides a random-like sample when the patient list has no hidden patterns. This multistage and systematic approach ensured that the sample was representative of the diabetic patient population across public hospitals in Addis Ababa, thereby improving the external validity and reliability of the study findings.

Inclusion criteria: All DM patients who have follow-up in the hospitals.

Exclusion criteria: Those patients who were critically ill, mentally unstable (an individual experience disturbances in emotional, cognitive, or behavioral functioning) or pregnant mothers were excluded.

Institutional Review Board Approval and Informed Consent

Ethical approval for this study was obtained from the Institutional Review Board. Written informed consent was obtained from all participants before the interviews.

Data Collection

Data was collected using a questionnaire administered through interviews (information related to socio-demographic characteristics, behavioral characteristics, and knowledge related to risk factors of CKD and extracted from the patient's medical record (such as length of time to diagnosis DM, co-morbidity rather than DM, and blood glucose level). The questionnaire was initially developed in English and then translated into Amharic. A bilingual expert reviewed the translations to ensure consistency. Additionally, a panel of experts assessed the validity of characteristics, lifestyle, predisposing factors, and knowledge questions, evaluating the tools for content validity, completeness, and clarity. Their feedback was then incorporated. Five BSc-trained nurse data collectors and one master of Public Health supervisor were involved in the data collection process.

Outcome

CKD is characterized by persistent abnormalities in kidney function lasting more than three months. It reduces the kidneys’ ability to eliminate waste products and excess fluid from the body efficiently. The outcome of measuring CKD prevalence is a clearer understanding of its magnitude, distribution, and risk profile, which guides evidence-based interventions and healthcare planning.

Study variable

Dependent variable

CKD in DM patients.

Independent variable

Socio-demographic variables: Sex, Age, Religion, Marital status, Occupation, Economic factor.

Lifestyle: Physical exercise, Clinical self-management, Dietary habits, Alcohol and drug use.

Knowledge: Consequences of uncontrolled DM, Role of Exercise in glycemic control, the impact of sugary diet.

Predisposing factors: Co-morbidity other than DM, Duration of DM, Home glucose monitoring, Drug utilization and duration of diagnosis.

Data Management and Analysis

The data entry was done using EpiInfo7 TM, and the analysis was done using SPSS version 26 Computer Software. Binary logistic regression for analysis was done to identify risk factors. Variables found to have an association with the dependent variable p-value less than .25, were entered into multivariable logistic regression analysis. Finally, the variables which showed a significant association were identified based on adjusted odds ratio (AOR) with a 95% confidence interval (CI). P-value less than .05 is used to report statically significance.

Results

Socio-Demographic Characteristics of the Study Participants

This study included 304 diabetic patients who met the inclusion criteria. More than half of the participants were aged between 30 and 64 years, with a mean age of 46.33 ± 19.36 years. Females made up 168 (55.3%) of the sample. Regarding marital status, 121 (39.8%) were married, and 115 (37.8%) had attained secondary-level education (Table 1).

Table 1.

Socio-demographic Characteristics of the Study Participants Among Diabetes Mellitus Patients (n = 304).

Socio demographic variables	Category	Frequency	Percentage
Age	<15	28	9.3
	15–29	48	15.8
	30–64	155	51.0
	>=65	73	24.0
Address	Addis Ababa	252	82.9
	Out of Addis	52	17.1
Sex	Female	168	55.3
	Male	136	44.8
Marital status	Divorce	28	9.2
	Married	121	39.8
	single	75	24.7
	widowed	80	26.3
Occupational status	Daily Labor	13	4.2
	Governmental employer	47	15.5
	Other	10	3.3
	Own business	112	36.8
Habit of alcohol consumption	Yes	64	21.1
	No	240	78.9
Educational status	Can read and write only	23	7.6
	Primary education (1 to 8)	71	23.4
	Higher level (Diploma & above	61	20.1
	Illiterate	34	11.2
Income level per month	Under 1000 birr	3	1.0
	from 1000 to 2000 birr	33	10.8
	from 2001 to 5000	115	37.8
	>5000 birr	153	50.3

Clinical-Related Characteristics of the Respondent

Regarding clinical history, the findings showed that over half of the respondents, 203 (56.3%), had been living with diabetes for more than 5 years. Only 171 (56.3%) of participants regularly and correctly monitored their blood glucose levels. Additionally, 235 (73.4%) of participants reported taking their prescribed medications as directed. Of the total participants, 135 (75.6%) of participants had a body mass index (BMI) within a normal range, and 250 (82.2%) had a co-morbid condition, primarily hypertension (Table 2).

Table 2.

Frequency Distribution According to Clinically Related Characteristics of Respondents Among Diabetes Mellitus Patients (n = 304).

Variables	Category	Frequency	Percentage
Perform physical activity	Yes	67	22.0
	No	237	78.0
Dietary habit	Eating any diet every day	38	12.5
	Eating sugar and fat diet occasionally	142	46.7
	Never eating a sugar and fat diet	124	40.8
Properly used ordered medication	No	69	22.7
	Yes	235	77.3
Properly monitor blood glucose levels	No	133	43.8
	Yes	171	56.3
Length of time to diagnosis DM	<5 years	101	33.2
	5–9 years	87	28.6
	10–14 years	79	26.0
	>14 years	37	12.2
Co-morbidity rather than DM	No anyone	54	17.8
	Previous hypertension	113	37.2
	Other chronic problems	45	14.8
	Previous asthmatic	59	19.4
	Previous HIV history	33	10.9
BMI	<18 kg/m²	39	12.8
	18 to 24.99 kg/m²	135	44.4
	25–29.9 kg/m²	135	44.4
	>30 kg/m²	10	8.60
Have you ever been diagnosed for kidney disease	No	182	59.9
	Yes	122	40.1
Do you have a history confirmatory diagnosis for chronic kidney disease	No	269	88.5
	Yes	35	11.5

BMI is a simple measure used to assess whether a person has a healthy body weight for their height. It helps categorize individuals as underweight (<18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight, or obese (>25.0 kg/m²).

Knowledge-Related Factors

In terms of participant knowledge, 113 individuals (37.2%) had some awareness of CKD, while the majority, 191 (62.8%), lacked any knowledge of the condition. Similarly, 170 participants (55.9%) were unaware of the causes associated with DM, a condition that is often difficult to manage.

Prevalence of CKD

The estimated prevalence of CKD in this investigation, as determined by glomerular filtration rate < 60 ml/min/1.73 m², was 35 (11.5%) (95% CI 9.21–13.41) of the total individuals.

Factors Associated With CKD Among DM Patients

In the bivariable analysis, the following variables had a p-value less than .25 and included in the multivariable logistic regression analysis: age, sex, educational status, place of residence, exercise habits, employment status, monthly household income, history of alcohol consumption, BMI, duration of DM, presence of co-morbidities other than diabetes, and dietary habits involving fat and sugar intake.

In the multivariable logistic regression analysis, seven variables were found to be statistically significantly associated with CKD at a p-value ≤ .05. These variables included co-morbidities, place of residence, physical activity, dietary habits, adherence to prescribed medications, BMI, and the duration of DM since diagnosis.

Patients with co-morbidities were three times more likely to develop CKD compared to those without (AOR = 3.0; 95% CI: 1.0–8.6). Those residing in urban areas had a sevenfold increased risk of CKD (AOR = 6.68; 95% CI: 1.9–22.9). Individuals who did not engage in physical activity were found to have approximately twice the risk of developing CKD compared to those who participated in regular physical exercise (AOR = 1.95; 95% CI: 1.3–11.5). Participants with a daily intake of sugar and fat were three times more likely to develop CKD (AOR = 3.0; 95% CI: 1.01–6.39).

Patients who did not adhere to their prescribed medications had an approximately sevenfold increased risk of CKD (AOR = 7.75; 95% CI: 1.31–18.58). CKD was also significantly associated with BMI; individuals with a BMI between 18 and 24.99 kg/m² were at higher risk compared to those with a BMI under 18 kg/m² (AOR = 2.05; 95% CI: 1.38–3.05).

Regarding the duration of diabetes, patients who had been diagnosed for 10–14 years and those with diabetes for more than 14 years had about six times higher risk of developing CKD compared to those with shorter disease durations (AOR = 6.04; 95% CI: 1.43–25.57 and AOR = 6.16; 95% CI: 1.03–36.89, respectively) (Table 3).

Table 3.

Factors Associated With CKD With Associated Factors Among Diabetes Patients, 2024 (n = 304).

Factors	Have CKD		COR (95% CI)	AOR (95% CI)	p-Value
Sex	Yes	No
Male	20	118	1.58 (0.78, 3.21)	0.02 (0.03, 1.93)	.17
Female	15	151	1
Co-morbidity
Hypertensive	13	110	3.57 (1.11, 11.55)	3.03 (1.0,8.6) *	.02
HIV patient	8	57	0. .85 (.277, 2.60)	0.38 (0.02, 6.73)
Asthmatics	6	67	0.68 (0.18, 2.52)	0.18 (0.01, 2.61)
Have no comorbid	8	35	1
Residence
Urban	22	230	0.29 (0.13, 0.62)	6.68 (1.9, 22.91) *	.003
Rural	13	39	1
BMI/obesity indicators
<18 kg/m²	15	35	1
18 to 24.99 kg/m²	6	118	0.18(0.07, 0.44)	2.05(1.38, 3.05) *	.010
25 to 29.99 kg/m²	9	107	0.13 (0.04, 0.36)	7.39 (1.04, 8.24)
>30 kg/m²	5	9	3.89 (0.82, 18.39)	1.23 (1.02, 3.83)
History of alcohol consumption
Yes	22	196	1
No	13	73	1.58 (0.76, 3.31)	3.13 (0.08, 7.93)
Physical activity
Yes	31	204	1
No	4	65	0.41 (0.14, 1.19)	1.95 (1.33, 11.49)*	.003
Duration of DM after diagnosis
<5 years	14	97	1
from 5 to 9 years	13	67	1.34 (0.59, 3.04)	3.24 (0.68, 15.37)
10 to 14 years	3	75	0.28 (0.08, 1.00)	6.04 (1.43, 25.57)*	.027
>14 years	5	30	1.15 (0.38, 3.47)*	6.16 (1.03, 36.89)*	.046
properly received prescribed medication
Yes	21	202	1
No	14	67	0.53 (0.254, 1.128)	7.75 (1.31, 18.50)*	.024
Dietary habit
Eating sugar and fat every day	36	4	0.58 (0.24, 1.37)	3 (1.01, 6.39)*	.047
Eating sugar and fat occasionally	133	7	2.56 (1.07, 6.13)	6.56 (0.32, 18.58)
Never eating sugar and fat	100	24	1

* p-value < .05.

Discussion

In this study, the prevalence of CKD among patients receiving follow-up care for diabetes was 11.5%, based on a confirmed diagnosis of CKD. Compared to previous studies, this prevalence is lower than several reported figures: 21.71% in a systematic review and meta-analysis from Ethiopia (Shiferaw et al., 2020), 10.8% in referral hospitals in the Amhara region, Ethiopia (Brennan et al., 2024), 25.0% in central South Africa (Fenta et al., 2023), 18.6% in Iran (Xie et al., 2023), 13.9% in the Republic of Macedonia (Bailey et al., 2018), and 43.5% in the United States (Shaw et al., 2020). Additionally, global estimates for 2010 and projections for 2030 suggest 69% prevalence in developing countries, with a 20% increase anticipated in developed countries (Naser et al., 2021). These discrepancies in CKD prevalence may be attributed to differences in case mix, sample size, diagnostic and laboratory procedures, genetic factors, the burden of risk factors, study design, and access to healthcare services.

This study provides strong evidence that living in an urban area is nearly seven times increased risk of CKD. This finding aligns with research conducted in Northern Tanzania (Stanifer et al., 2018). Overall, multiple studies across different regions consistently identify urbanization as a significant risk factor for CKD, likely due to a combination of lifestyle changes and environmental exposures associated with urban living.

A significant association was found between CKD and a history of hypertension. In this study, individuals with a history of hypertension were three times more likely to develop CKD compared to those without such a history. This finding is consistent with studies conducted in the northern region of Ethiopia (Alemu et al., 2020). The association may be explained by the presence of comorbidities among individuals with hypertension, which increases their susceptibility to CKD. The history of overweight has a high correlation with chronic renal disease. Compared to individuals without a history of overweight, this result is consistent with research from the Police Hospital in Addis Ababa, Ethiopia (Middleton et al., 2018). In some cases, improper use of medications can exacerbate CKD. Typically, underlying chronic conditions such as diabetes and hypertension are the primary contributors to the development and progression of CKD.

Regular consumption of a diet with heavy in fat and sugar is linked to a higher risk of CKD. Individuals with a daily consumption of fat and sugar had a 3.0 times higher risk of developing CKD than those without such a daily consumption. The American National Health and Nutrition Examination Survey supports this conclusion (Lin et al., 2022). Consuming a diet heavy in fat and sugar regularly, particularly from highly processed foods, is linked to an increased risk of CKD.

Regular physical activity was identified as a significant factor in this study. Physically inactive individuals had nearly twice the risk of developing CKD compared to those who engaged in regular physical activity. The findings are consistent with previous research conducted at Dessie Referral Hospital in Ethiopia (Adem et al., 2024). This association may be attributed to exercise improves cardiovascular health, blood pressure, and blood sugar control, all of which are important factors in kidney health.

Strengths and Limitations of the Study

Strengths of the study: In this study, careful attention was given to evaluation criteria that were consistent with the selected study design, ensuring methodological appropriateness and accurate interpretation of the results within the context of the research approach.

Limitations of the study: This study employed a cross-sectional design, which measures exposure and outcome simultaneously, making it impossible to determine a temporal relationship between them. It is also subject to recall bias, as participants may not accurately report past exposures, and controlling for all potential confounding variables is challenging. Additionally, the small size of the CKD subgroup limited the ability to compare subgroups or adjust for multiple variables. In addition, in this study, the authors did not use imaging modalities for structural abnormalities of the kidney for the diagnosis of CKD among diabetes patients, which limits the ability to make a definitive diagnosis of diabetes-related kidney disease.

Implications for Nursing Practice

The findings highlight several modifiable factors associated with CKD among patients with DM, underscoring the vital role of nursing in prevention and long-term management. The nurses emphasize the holistic and patient-centered care, these results call for early identification and management of comorbidities through comprehensive assessment and intervention. Nurses are uniquely positioned to provide structured lifestyle counselling, promote regular physical activity, and deliver tailored dietary education to reduce CKD risk. Furthermore, incorporating routine follow-up, counselling, and adherence support—through reminders, family engagement, and digital health tools—aligns with the nursing focus on sustained behavioral change and self-management. Regular kidney function screening should also be integrated into nursing practice as part of on-going health surveillance. Overall, the findings reinforce the importance of a nursing-led, comprehensive diabetes management approach that combines medical care, lifestyle modification, and continuous monitoring to prevent or delay CKD progression.

Conclusion

In this study, the prevalence of CKD among patients with diabetes was found to be 11.5%, which is relatively lower than figures reported in several national and international studies. Despite the variation in prevalence across settings, the current findings reaffirm that CKD remains a significant complication of diabetes requiring urgent attention.

The study identified several factors significantly associated with CKD, including urban residence, hypertension, overweight, dietary habits involving high fat and sugar intake, lack of physical activity, and poor adherence to prescribed medications. These results are consistent with previous studies and highlight the importance of both clinical management and lifestyle modification in reducing the risk of CKD.

Overall, the findings underscore the need for comprehensive diabetes care strategies that integrate early detection of comorbidities, continuous patient education on healthy lifestyle practices, promotion of medication adherence, and regular screening for CKD, particularly among high-risk groups. Strengthening preventive and management strategies at both community and healthcare system levels is critical to reducing the burden of CKD among diabetic patients.

Footnotes

Acknowledgments

The authors would like to thank the staff and administrators of Menelik II Referral Hospital, Yekatete 12 Hospital, Ras Desta Dametew Hospital, and Zewditu Memorial Hospital for their collaboration and unreserved help during data collection.

ORCID iDs

Esubalew Tesfahun

Fitsum Zekariyas Mohammed

Ethical Considerations

Ethical approval for this study was obtained from the Institutional Review Board of Debre Berhan University, Asrat Woldeyes Health Science Campus, before its initiation (Ref. No./4340/227). Additionally, an official letter of permission was secured from the study hospitals before data collection began. Before conducting interviews, data collectors explained the study's purpose, potential benefits, risks, possible discomforts, and participants’ rights to refuse or withdraw at any time without consequence. Written informed consent was obtained from all participants before the interviews. Participant privacy and confidentiality were strictly maintained throughout the data collection process. Identities were not disclosed, and all responses remained anonymous.

Author Contributions

Esubalew Tesfahun conceptualized the study, conducted the statistical analysis, interpreted, and drafted the manuscript. Tessema Bayu were involved in designing the study, conducting the statistical analysis, and critical revision for important intellectual content. Fitsum Zekariyas and Dereje Andargie made a substantial contribution to the conception and supervised the data collection and interpretation of data. All the authors read and approved the final manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability

The data used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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