Understanding the healthcare services and social support needs of patients with end-stage kidney disease in Ghana

Abstract

Background

End-Stage Kidney Disease (ESKD) is a significant health issue that poses various social and healthcare challenges. Patients with ESKD often struggle to access and utilize hemodialysis services, which are crucial for disease management. This study assessed the healthcare services and social support needs of patients with ESKD who were receiving or in need of hemodialysis at two teaching hospitals in Ghana.

Methods

A descriptive cross-sectional study was conducted involving 264 patients with ESKD from Korle Bu Teaching Hospital (KBTH) and Tamale Teaching Hospital (TTH). Participants were selected using proportionate stratified and convenience sampling techniques. Eligible participants were adults (≥ 18 years) with ESKD who were receiving or in need of hemodialysis and could communicate in English, Akan, Dagbani, or Ga. Patients on peritoneal dialysis or with a history of kidney transplant were excluded. Participants completed structured questionnaires assessing their healthcare services and social support needs. Data were analyzed using IBM SPSS Statistics version 27.

Results

The study showed that most patients with ESKD (83.3%) faced significant financial challenges in accessing hemodialysis. Social support needs were evident, with 36.0% sometimes requiring financial assistance and 33.7% consistently needing help with daily chores. Healthcare access challenges were also widespread, with most participants skipping necessary care due to cost and experiencing difficulties related to travel distance.

Conclusion

This study highlights critical challenges faced by patients with ESKD, particularly financial constraints and healthcare access issues, which impact treatment adherence and utilization. Addressing financial barriers and improving healthcare accessibility could enhance hemodialysis service utilization and overall patient outcomes.

Introduction

Chronic kidney disease (CKD) is a major public health concern, with a global prevalence of 10–13% based on data up to 2022 [1]. Patients with CKD eventually progress to end-stage kidney disease (ESKD), which is characterized by permanent kidney damage, loss of renal function, and a glomerular filtration rate (GFR) of less than 15 mL/min/1.73 m² [2]. Ghana is experiencing a rising prevalence of ESKD, primarily due to increasing rates of hypertension and diabetes mellitus [3]. The estimated prevalence of CKD in Ghana is 13.3% [4, 5], accounting for 5% of hospital admissions [6].

The management of ESKD requires either conservative (palliative) care or renal replacement therapy, which includes hemodialysis, peritoneal dialysis, or kidney transplantation. In Ghana, hemodialysis is the most commonly utilized treatment modality for ESKD [6, 7]. However, various systemic and socioeconomic factors influence patient access to and utilization of hemodialysis services [8, 9]. Systemic factors, such as the availability of hemodialysis services [10] and the presence of healthcare professionals trained to deliver these services [11], along with socioeconomic factors, including income level, education, and social support networks, significantly impact patients’ access to and utilization of hemodialysis [12, 13].

Patients undergoing hemodialysis face substantial social and healthcare challenges [14, 15]. Social networks, family dynamics, and local resources influence patients’ ability to manage their condition and adhere to treatment regimens, making social support a crucial component of ESKD management [16, 17]. Additionally, traditional beliefs, religious practices, and cultural norms in African countries such as Ghana shape patients’ perceptions of illness and medical care, ultimately affecting their experiences and treatment decisions [18–20].

Understanding healthcare services and the social care needs of patients with ESKD is essential for developing effective interventions that address these needs and thereby improve patient outcomes [21]. In Ghana, regional disparities in access to hemodialysis services are particularly pronounced. Korle Bu Teaching Hospital (KBTH), located in the capital, is better resourced and offers specialized services, while Tamale Teaching Hospital (TTH) in Northern Ghana operates with significantly fewer resources. These differences underscore the importance of assessing disparities in access to healthcare services and social support needs across different regions and healthcare settings [22]. This study, guided by Anderson’s model, addresses these gaps by assessing healthcare services and social support needs of patients with ESKD who were receiving or in need of hemodialysis at two teaching hospitals in Ghana. This study aimed to answer the following research questions:

1. What are the health services and social support needs of patients with ESKD?

2. What proportion of patients with ESKD in the study sample are currently receiving hemodialysis?

3. What barriers do patients with ESKD encounter in accessing hemodialysis services?

4. Are there differences in access to healthcare services among patients with ESKD between KBTH and TTH?

5. Are there differences in sociodemographic characteristics among patients with ESKD between KBTH and TTH?

6. Are there differences in clinical and treatment characteristics among patients with ESKD between KBTH and TTH?

7. Are there differences in social support and functional well-being among patients with ESKD between KBTH and TTH?

Theoretical framework

This study is based on a widely used framework for examining healthcare access and use: the Andersen Behavioral Model of Health Services Use. According to Andersen [23], the model identifies three areas as the determinants of healthcare use: predisposing factors (e.g., age, gender, education), enabling factors (e.g., income, social support, transportation, availability of services), and need factors (e.g., perceived health status, severity of disease) [24, 25].

This model is particularly helpful for understanding how people engage with healthcare systems and decide whether to seek medical care. It facilitates the examination of how social contexts (such as support networks), clinical demands, and structural constraints (such as resource limitations) interact to influence patients’ experiences and care-seeking behaviors in the context of ESKD and hemodialysis in Ghana [24, 26].

Although the Andersen model has been extensively applied in research on chronic illness and healthcare utilization globally, its application in sub-Saharan African nephrology studies remains limited [26, 27]. By using this model, the current study helps close this gap by assessing healthcare services and the social care needs of patients with ESKD. This theory-driven approach facilitates the development of focused, evidence-based interventions to enhance access to care, improve service utilization, and address the social support needs of this population [24].

The model informed multiple stages of this research, from instrument development to data interpretation and discussion. Survey instruments were designed to capture predisposing factors (e.g., sociodemographic characteristics), enabling factors (e.g., costs and service availability), and need-related factors (e.g., comorbidities), in line with the study’s focus on healthcare services and social support needs. During the analysis and discussion phases, the model provided a systematic lens through which patterns of access to healthcare services and social support needs among patients with ESKD undergoing hemodialysis in Ghana were identified and interpreted (Fig. 1).

Fig. 1.

Behavioral model of health services use based on a model adapted from Andersen and Aday, 1995

Materials and methods

Study design and period

This study adopted a descriptive cross-sectional design [28, 29], following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. The study was conducted between October 2023 and December 2023 at two teaching hospitals in Ghana, one in the southern region and the other in the northern region.

Study settings

This study was conducted at two teaching hospitals in Ghana: Korle Bu Teaching Hospital (KBTH) in Accra and Tamale Teaching Hospital (TTH) in Tamale. Both hospitals serve as national referral centres and provide hemodialysis services to patients with ESKD. During the study period, KBTH had 4 nephrologists, 55 dialysis nurses, and 19 dialysis machines, while TTH had 1 nephrologist, 26 dialysis nurses, and 11 dialysis machines. KBTH had 470 patients with ESKD, while TTH had 310. In total, the combined number of patients with ESKD across the two hospitals was 780.

Population and eligibility criteria

The target population for the study were patients with ESKD seeking healthcare services at the two teaching hospitals in Ghana. Eligible participants (1) had ESKD and were receiving or in need of hemodialysis treatment, (2) were at least 18 years old, and (3) were able to communicate in the English language and/or Akan, Dagbani, and Ga. The exclusion criteria were patients receiving peritoneal dialysis treatment or those who had experienced a kidney transplant, because their experiences with renal replacement therapy differ significantly from those undergoing hemodialysis.

Sample size determination

The minimum sample size of 264 participants was determined using Yamane’s formula [30], which estimates sample size based on a known population: , The study population consisted of 780 patients with end-stage kidney disease (ESKD), and a margin of error (e) of 0.05 was applied. The sample size (n) was calculated as follows: n = 780/ (1+780(0.05)², n = 780/ (1 + 780(0.0025), n = 780/ (1+1.95), n = 780/2.95, n = 264.

To ensure proportional representation from the two hospital sites, a proportionate stratified random sampling technique was employed. The sample size for each hospital was determined using the formula: x n, where n_h represents the sample size for each stratum (hospital), N_h is the population of each hospital, N is the total population, and n is the overall sample size. Based on this calculation, 159 patients were selected from KBTH (470/780 × 264) and 105 from TTH (310/780 × 264), reflecting their respective proportions within the total ESKD population (Fig. 2).

Fig. 2.

Schematic diagram of the sampling procedure for the study population in the two hospitals in Ghana

Recruitment

Convenience sampling was employed to recruit participants who were readily available and willing to participate in the study. The charge nurses of the renal units at the two hospital sites assisted with initial recruitment. The researchers explained the study to the charge nurses and provided them with a written summary of the study protocol, including the inclusion and exclusion criteria and the researchers’ (MJ and MMI) contact information. The charge nurses pre-screened patients based on these criteria and identified potentially eligible participants, who were then informed about the study and asked for their permission to be approached by the researchers. The researchers visited the hospitals and met with the potential participants identified by the charge nurses. Detailed information was provided to the patients regarding the study objectives, data collection procedures, the rationale for the study, and what participation would entail. The researchers also independently confirmed each patient’s eligibility. Informed consent was obtained from participants who agreed to take part in the study.

Instrument and study variables

Data were collected using a structured questionnaire. The instrument integrated items from two validated tools: the End-Stage Renal Disease Adherence Questionnaire (ESRD-AQ) developed by Kim et al. [31] and the RAND Health Care for Communities Household Survey (HCSUS) Baseline Questionnaire [32]. The questionnaire was further refined through an extensive review of relevant literature to ensure contextual relevance. Additionally, pilot testing for clarity and comprehension was conducted with 10 participants prior to the main data collection. The questionnaire comprised three sections (A, B, and C). Section A captured sociodemographic characteristics, including participants’ age, sex, ethnicity, marital status, level of education, religion, employment status, monthly family income, travel distance to the hospital, area of residence, and number of children.

Section B assessed participants’ clinical and treatment-related characteristics, including duration of the condition, cause of the condition, and access to and utilization of hemodialysis services. Other variables included duration on hemodialysis, prescribed number of sessions per week, and attendance at prescribed sessions. These items were adapted from the ESRD-AQ. The ESRD-AQ has demonstrated acceptable psychometric properties, with previous studies reporting internal consistency reliability (Cronbach’s alpha) values ranging from 0.63 to 0.81, thereby supporting its suitability for use in hemodialysis populations [33–35]. In this study, adherence was measured using participants’ self-reported attendance at prescribed dialysis sessions over the past four weeks and the past week. Participants were further categorized based on whether they missed any sessions and the number of sessions missed.

Section C incorporated selected modules from the RAND HCSUS Baseline Questionnaire, also a validated instrument widely used to assess healthcare access, social support, and health-related quality of life among individuals with chronic conditions [36, 37]. For the purposes of this study, relevant items were adapted to evaluate three key domains: social support, functional well-being, and access to healthcare services. Each domain consisted of multiple items measured using 3-, 5-, or 6-point Likert-type scales. Responses were aggregated by averaging to generate composite scores for each domain, enabling the derivation of an overall score representing participants’ general experiences within each construct. In this scoring format, higher scores reflected greater perceived social support, higher functional well-being, and greater access to healthcare services. Importantly, negatively worded items within the social support domain were identified and confirmed through exploratory factor analysis (EFA) and were subsequently reverse coded prior to analysis to ensure conceptual consistency across all items. This procedure ensured that all items were aligned in the same positive direction for accurate interpretation.

Validity and reliability

Content and face validity of the study instrument, adapted from the ESRD-AQ [31] and the RAND HCSUS Baseline Questionnaire [32], were assessed by a panel of five experts. The experts evaluated each item for clarity, relevance, and appropriateness within the Ghanaian context, as well as the overall structure, flow, and formatting of the questionnaire. Test–retest reliability was assessed using a subset of patients with ESKD who completed the questionnaire twice over a two-week interval. The instrument demonstrated high test–retest reliability, with a correlation coefficient of 0.87, indicating consistent responses across both administrations.

Data collection procedure

Consenting participants completed an online forced-choice questionnaire with the assistance of the researchers using the researchers’ mobile phones. The survey was administered in English via the Qualtrics platform (Qualtrics Survey, 2023). Participants who were proficient in English completed the questionnaire independently; however, for those who experienced difficulties reading or navigating the digital interface, the researchers read the questions aloud and entered responses directly into the online system. For participants with limited English proficiency, trained researchers provided oral translation of the questionnaire into Akan, Dagbani, or Ga and subsequently recorded participants’ verbal responses into the online form. The survey required approximately 30 to 45 min to complete. All participants received $10 (GHC 70) as compensation for their time and participation in the study.

Data analysis

Data were exported from Qualtrics and imported into JMP Professional 17.1 (SAS Institute Inc., Cary, North Carolina, USA) for data screening and cleaning. Statistical analyses were performed using IBM SPSS Statistics 27 (IBM Corp., Armonk, New York, USA). Descriptive statistics, including frequencies, percentages, means, and standard deviations, were used to summarize the characteristics of the study sample across all variables. Independent samples t-tests were conducted to examine differences in access to healthcare services between patients at KBTH and TTH. The chi-square test of association was also used to assess differences in proportions for sociodemographic characteristics, clinical and treatment characteristics, as well as social support and functional well-being between patients at KBTH and TTH. A p-value of < 0.05 was considered statistically significant.

Ethical consideration

Ethical approval for the study was obtained from the Human Participants Review Sub-Committee of York University’s Ethics Review Board under protocol number [STU 2023-082] in Toronto, Canada, as well as the Korle Bu Teaching Hospital Scientific and Technical Committee/Institutional Review Board (KBTH/IRB/000135/2023) in Ghana. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (2013) and the Canadian Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS 2, 2022). Participation was voluntary, and informed consent was obtained from all participants prior to data collection. Participants were assured of confidentiality, anonymity, and their right to withdraw from the study at any time without any consequences to their care. All data were securely stored and accessed only by the research team for the purpose of the study.

Results

Sociodemographic characteristics

A total of 264 individuals were approached for participation in the study, and all 264 completed the survey, yielding a response rate of 100%. The average age of participants was 46.7 years (SD: 14.8). Most participants (54.2%) were married, 35.6% had a post secondary (tertiary) education, and 29.9% were unemployed, 32.6% of participants had a monthly family income of GHC 1,000 or less, 66.7% resided in urban areas, and 37.9% had three or more children. The average distance traveled by participants to access hemodialysis services was approximately 54.0 km (SD = 27.5) (Table 1).

Table 1.

Sociodemographic characteristics of the participants (N = 264)

Variable	Frequency (n)	Percentage (%)	Mean	SD
Age			46.7	14.8
18–30 years	42	15.9
31–40 years	44	16.7
41–50 years	67	25.4
51–60 years	53	20.1
> 60 years	58	22.0
Sex
Male	152	57.6
Female	112	42.4
Ethnicity
Akan	78	29.5
Dagomba	40	15.2
Ewe	25	9.5
Frafra	7	2.7
Ga	31	11.7
Ga Adangbe	13	4.9
Gonja	12	4.5
Hausa	10	3.8
Kassena	5	1.9
Konkomba	8	3.0
Krobo	4	1.5
Mamprusi	10	3.8
Othersa	21	8.0
Marital status
Single	61	23.1
Married	143	54.2
Informal/Living together	10	3.8
Divorced	24	9.1
Widowed	17	6.4
Separated	9	3.4
Level of education
No formal education	47	17.8
Primary	49	18.6
Secondary	74	28.0
Tertiary	94	35.6
Religion
Christianity	152	57.6
Islam	92	34.8
Non-denominational	12	4.5
No religion	4	1.5
Traditional	4	1.5
Employment status
Unemployed	79	29.9
Retired	42	15.9
Self-employed	70	26.5
Private employee	28	10.6
Public employee	45	17.0
Monthly family income (GHC)
No income	54	20.5
≤ 1000	86	32.6
1001–3000	78	29.5
3001–5000	29	11.0
5001–7000	10	3.8
> Ghc7000	7	2.7
Travel distance to hospital (km)			54.0	27.5
0–20 km	41	15.5
21–40 km	41	15.5
41–60 km	71	26.9
61–80 km	46	17.4
> 80 km	65	24.6
Area of residence
Rural	88	33.3
Urban	176	66.7
Number of children
None	57	21.6
One	36	13.6
Two	71	26.9
Three or more	100	37.9

Note: GHC = Ghana Cedis; km = kilometers

Clinical and treatment characteristics

The majority of participants (n = 196, 74.2%) were receiving hemodialysis, with 36.7% undergoing treatment for 3 months to 1 year. About 61.2% of participants were prescribed hemodialysis treatments three times per week, and most (53.6%) had prescribed session durations of 3 h. Among the 196 participants receiving hemodialysis, only 38.8% adhered to all prescribed dialysis sessions in the last 4 weeks, while 54.6% reported not attending all prescribed dialysis sessions in the last week (Table 2).

Table 2.

Clinical and treatment characteristics among participants (N = 264)

Variable	Frequency (n)	Percentage (%)
Duration of condition
< 3 months	69	26.1
3 months – 1 year	94	35.6
1–2 years	44	16.7
2–3 years	32	12.1
3–5 years	16	6.1
> 5 years	9	3.4
Cause of condition +
Hypertension	162	61.3
Diabetes	56	21.2
Unknown	65	24.6
Glomerulonephritis	13	4.9
Herbal medicine	13	4.9
Genetic	5	1.9
Systemic lupus erythematosus	1	0.4
Receiving hemodialysis for kidney disease
Yes	196	74.2
No	68	25.8
Hospital where care was received
Korle Bu Teaching Hospital	159	60.2
Tamale Teaching Hospital	105	39.8
Duration of receiving hemodialysis ( N  = 196)
< 1 month	42	21.4
3 months – 1 year	72	36.7
1–2 years	37	18.9
2–3 years	23	11.7
3 years – 5 years	13	6.6
> 5 years	9	4.6
Number of days a week a doctor recommended dialysis treatment
≤ 2 days	73	37.3
3 days	120	61.2
4 days	3	1.5
Able to attend all prescribed dialysis sessions in the last 4 weeks ( N  = 196)
Yes	76	38.8
No	120	61.2
Missed any dialysis sessions in the last week ( N  = 196)
Yes	107	54.6
No	89	45.4
Number of hours spent for each dialysis session ( N  = 196)
3 h	105	53.6
4 h	73	37.2
5 h	14	7.1
> 5 h	4	2.0
Number of prescribed weekly dialysis sessions missed last week ( N  = 107)
Missed 1	72	67.3
Missed 2	31	29.0
Missed 3	3	2.80
Missed 4	1	0.9

Note: ⁺Participants could select more than one response

Level of social support needs and functional well-being

Overall, participants reported a mean social support score of 3.10 (SD = 0.72) on a 5-point scale, with scores ranging from 1 to 5. This reflects a moderate to high level of perceived social support among the participants. The mean scores for functional well-being also indicated that many participants experienced significant interference from physical or emotional problems in their daily and social activities (mean = 4.25, SD = 1.23). Notably, 63.6% of participants reported that their health prevented them from working at a job, doing work around the house, or going to school all the time. Detailed frequencies for individual items assessing social support and functional well-being are presented in Table 3.

Table 3.

Level of social support needs and functional well-being among the participants (N = 264)

Variable	Frequency (n)	Percentage (%)	Mean	SD
Social support during the past four weeks:
Someone to give you money if you need it			3.07	1.29
None of the time	49	18.6
A little of the time	22	8.3
Some of the time	95	36.0
Most of the time	55	20.8
All of the time	43	16.3
Someone to help with daily chores if you were sick			3.79	1.07
None of the time	5	1.9
A little of the time	26	9.8
Some of the time	76	28.8
Most of the time	68	25.8
All of the time	89	33.7
Someone to love and make you feel wanted			3.54	1.08
None of the time	4	1.5
A little of the time	41	15.5
Some of the time	95	36.0
Most of the time	54	20.5
All of the time	70	26.5
Frequency of seeing or hearing from:
Relatives			2.13	1.15
Less than once a month	15	5.7
About once a month	19	7.2
A few times a month	49	18.6
A few times a week	85	32.2
Every day	96	36.4
Close friends			3.14	1.26
Less than once a month	56	21.2
About once a month	45	17.0
A few times a month	64	24.2
A few times a week	79	29.9
Every day	20	7.6
Functional well-being during the past four weeks:
Extent at which physical health or emotional problems interfered with normal social activities with family, friends, neighbors, or groups			4.25	1.23
None of the time	7	2.7
A little of the time	13	4.9
Some of the time	30	11.4
A good bit of the time	38	14.4
Most of the time	91	34.5
All of the time	85	32.2
Frequency of physical health or emotional problems interfering with social activities (such as visiting with friends, relatives, etc.)			4.05	1.05
None of the time	5	1.9
A little of the time	11	4.2
Some of the time	48	18.2
Most of the time	103	39.0
All of the time	97	36.7
Health has kept you from working at a job, doing work around the house, or going to school
No	20	7.6
Yes, for some of the time	76	28.8
Yes, for all of the time	168	63.6

Access to healthcare services

Independent samples t-tests were conducted to examine differences in access to healthcare services between participants at KBTH and TTH. Participants at KBTH reported significantly better overall access to healthcare services for their kidney disease (M = 2.84, SD = 0.99) compared to those at TTH (M = 2.51, SD = 0.88). The mean difference was 0.33 points [95% CI (0.09, 0.56)]. This difference was statistically significant, t(262) = 2.74, p = 0.006, with a Cohen’s d of 0.35 [95% CI (0.10, 0.59)], indicating a small-to-moderate effect size (Table 4).

Table 4.

Difference in access to healthcare services by hospital (N = 264)

Variable		t-test for Equality of Means					95% CI
	Mean ± SD	T	df	P value	Mean difference	Std. Error difference	Lower	Upper
Region		2.74	262	0.006*	0.33	0.12	0.09	0.56
Korle Bu Teaching Hospital (KBTH)	2.84 ± 0.99
Tamale Teaching Hospital (TTH)	2.51 ± 0.88

Note: *Statistically significant at p < 0.05, Assuming unequal variances

At the item level, participants at KBTH reported significantly higher agreement that they could be admitted to the hospital without difficulty if they needed hospital care for their kidney disease (M = 3.34, SD = 1.51) compared to those at TTH (M = 2.90, SD = 1.40), t(233.6) = -2.39, p = 0.017. Conversely, participants at TTH reported a higher mean score for sometimes going without the medical care they needed for their kidney disease because it was too expensive (M = 4.22, SD = 1.34) than those at KBTH (M = 3.79, SD = 1.54), t(243.2) = 2.42, p = 0.016. Significant differences were also found in perceptions of care convenience and availability. KBTH participants reported higher mean scores indicating that places where they could receive medical care for their kidney disease were conveniently located (M = 3.15, SD = 1.39) compared to TTH participants (M = 2.40, SD = 1.45), t(216.2) = -4.18, p < 0.001. KBTH participants also reported higher mean scores indicating that they could receive medical care for their kidney disease whenever they needed it (M = 3.01, SD = 1.39) compared to TTH participants (M = 2.50, SD = 1.32), t(230.7) = -2.94, p = 0.004. No significant differences were observed between KBTH and TTH with regard to emergency care access, access to medical specialists, or distance-related barriers (p > 0.05 for all) (Table 5).

Table 5.

Access to healthcare services among the participants (N = 264)

Variable	Total Mean (SD)	KBTH Mean (SD)	TTH Mean (SD)	t (df)	p-value
1. If I need hospital care for my kidney disease, I can get admitted without any trouble.	3.16 ± 1.48	3.34 ± 1.51	2.90 ± 1.40	-2.39 (233.6)	0.017*
2. It is hard for me to get medical care for my kidney disease in an emergency.	3.18 ± 1.29	3.24 ± 1.37	3.10 ± 1.16	-0.85 (245.5)	0.394
3. Sometimes I go without the medical care I need for my kidney disease because it is too expensive.	3.95 ± 1.47	3.79 ± 1.54	4.22 ± 1.34	2.42 (243.2)	0.016*
4. I have easy access to the medical specialists I need for my kidney disease.	2.79 ± 1.43	2.88 ± 1.45	2.67 ± 1.41	-1.19 (227.0)	0.234
5. Places where I can get medical care, for my kidney disease are very conveniently located.	2.85 ± 1.46	3.15 ± 1.39	2.40 ± 1.45	-4.18 (216.2)	< 0.001*
6. I can get medical care for my kidney disease care whenever I need it.	2.80 ± 1.38	3.01 ± 1.39	2.50 ± 1.32	-2.94 (230.7)	0.004*
7. At times I do not have access to my kidney disease treatment because of the distance I have to travel to the hospital.	3.46 ± 1.53	3.43 ± 1.49	3.52 ± 1.61	0.49 (210.9)	0.626

Note: *Statistically significant at p < 0.05

Difference in sociodemographic characteristics by hospital

A chi-square test was conducted to examine proportional differences in sociodemographic characteristics between participants receiving hemodialysis at KBTH and TTH. Marital status differed significantly between the two hospitals, χ² = 19.46, p = 0.001. At TTH, a higher proportion of participants were married (67.6%) compared to KBTH (45.3%). Employment status also showed a statistically significant difference, χ² = 11.40, p = 0.022. At TTH, a larger proportion of participants were unemployed (37.1%) compared to KBTH (25.2%). Additionally, retired participants were more prevalent at KBTH (21.4%) than at TTH (7.6%). Area of residence varied significantly by hospital, χ² = 20.57, p = 0.001. Nearly half of the participants at TTH resided in rural areas (49.5%), which was substantially higher than the proportion observed at KBTH (22.6%) (Table 6).

Table 6.

Difference in sociodemographic characteristics by hospital (N = 264)

Variable	KBTH n (%)	TTH n (%)	X2	p-value
Marital status			19.46	0.001*
Single	41 (25.8)	20 (19.1)
Married	72 (45.3)	71 (67.6)
Informal/Living together	9 (5.7)	1 (1.0)
Divorced	14 (8.8)	10 (9.5)
Widowed	15 (9.4)	2 (1.9)
Separated	8 (5.0)	1 (1.0)
Employment status			11.40	0.022*
Unemployed	40 (25.2)	39 (37.1)
Retired	34 (21.4)	8 (7.6)
Self-employed	44 (27.7)	26 (24.8)
Private employee	15 (9.4)	13 (12.4)
Public employee	26 (16.4)	19 (18.1)
Area of residence			20.57	0.001*
Rural	36 (22.6)	52 (49.5)
Urban	123 (77.4)	53 (50.5)

Note: *Statistically significant at p < 0.05; Only five variables are presented in this table; other variables not shown were statistically non-significant

Difference in clinical and treatment characteristics by hospital

Differences in clinical and treatment characteristics between participants at KBTH and TTH were assessed using chi-square test of association analysis. A statistically significant variation was observed in the number of days per week that dialysis treatment was prescribed for participants, χ² = 8.41, p = 0.004. At TTH, a higher proportion of participants were prescribed dialysis for 2 days or fewer per week (48.8%) compared to those at KBTH (28.6%). In contrast, no statistically significant differences were found between the two hospitals with respect to receiving hemodialysis for kidney disease, the ability to attend all prescribed dialysis sessions in the past four weeks, whether participants missed any dialysis sessions in the preceding week, and the number of prescribed sessions missed during that period. The distribution of these variables was comparable across both hospitals within each group (Table 7).

Table 7.

Difference in clinical and treatment characteristics by hospital (N = 264)

Variable	KBTH n (%)	TTH n (%)	X2	p-value
Receiving hemodialysis for kidney disease			3.02	0.082
Yes	112 (70.4)	84 (80.0)
No	47 (29.6)	21 (20.0)
Number of days a week a doctor recommended dialysis treatment**			8.41	0.004*
≤ 2 days	32 (28.6)	41 (48.8)
≥ 3 days	80 (71.4)	43 (51.2)
Able to attend all prescribed dialysis sessions in the last 4 weeks			0.58	0.446
Yes	46 (41.1)	30 (35.7)
No	66 (58.9)	54 (64.3)
Missed any dialysis sessions in the last week			1.25	0.263
Yes	65 (58.0)	42 (50.0)
No	47 (42.0)	42 (50.0)
Number of prescribed weekly dialysis sessions missed last week**			0.54	0.463
Missed 1	42 (64.6)	30 (71.4)
Missed 2 or more	23 (35.4)	12 (28.6)

Note: *Statistically significant at p < 0.05; Only five variables are presented in this table; other variables not shown were statistically non-significant. **Variable categories were recoded

Difference in social support and functional well-being by hospital

Chi-square analyses were conducted to compare social support and functional well-being among participants receiving hemodialysis at KBTH and TTH. Access to financial support showed a significant difference between the two hospitals, χ² = 20.21, p = 0.001. At KBTH, a larger share of participants reported having someone to provide money “all of the time” (23.3%), whereas only 5.7% of participants at TTH reported the same level of support. Functional well-being, assessed by the extent to which physical health or emotional problems interfered with social activities, also differed significantly, χ² = 14.03, p = 0.015. At KBTH, 34.0% of participants reported interference “most of the time”. Other measures of social support and functional well-being were examined but showed no statistically significant differences between the two hospitals (Table 8).

Table 8.

Difference in social support and functional well-being by hospital (N = 264)

Variable	KBTH n (%)	TTH n (%)	X2	p-value
Social support during the past four weeks:
Someone to give you money if you need it			20.21	0.001*
None of the time	22 (13.8)	27 (25.7)
A little of the time	13 (8.2)	9 (8.6)
Some of the time	50 (31.5)	45 (42.9)
Most of the time	37 (23.3)	18 (17.1)
All of the time	37 (23.3)	6 (5.7)
Functional well-being during the past four weeks:
Extent at which physical health or emotional problems interfered with normal social activities with family, friends, neighbors, or groups			14.03	0.015*
None of the time	7 (4.4)	0 (0.0)
A little of the time	12 (7.6)	1 (1.0)
Some of the time	18 (11.3)	12 (11.4)
A good bit of the time	25 (15.7)	13 (12.4)
Most of the time	54 (34.0)	37 (35.2)
All of the time	43 (27.0)	42 (40.0)

Note: *Statistically significant at p < 0.05. Only variables with statistically significant associations are presented

Barriers to hemodialysis utilization

In this study, the most frequently reported barriers to utilizing hemodialysis services among participants were financial constraints (83.3%) and long distance from the dialysis facility (21.2%). The least reported barrier was unawareness of the necessity for care (0.4%) (Fig. 3).

Fig. 3.

Barriers to hemodialysis utilization among the participants (N = 264)

Discussion

This study is the first to assess the healthcare services and social support needs among patients with ESKD who were receiving or in need of hemodialysis at two teaching hospitals in Ghana: Korle-Bu Teaching Hospital (KBTH) and Tamale Teaching Hospital (TTH). It is important to note that in Ghana, hemodialysis is not covered under the national health insurance scheme and is not funded by tax dollars, requiring patients to pay out of pocket [5, 6, 38]. Sub-Saharan African countries use various approaches to provide hemodialysis services for patients with kidney failure. These include government subsidies for hemodialysis (Senegal, Ethiopia, Cameroon), universal coverage for acute kidney failure only (South Africa, Ethiopia), and state coverage for chronic hemodialysis under limited conditions (South Africa). Other countries, such as Ghana, Nigeria, Burundi, and the Democratic Republic of the Congo, do not offer state coverage for hemodialysis [39, 40]. Similarly, in Mexico, Agudelo-Botero et al. [41] and Azizi et al. [42] in Iran found that hemodialysis treatment is not funded. This creates a substantial financial burden, severely limiting access to regular and timely care for many patients with ESKD.

The study results revealed significant challenges in hemodialysis treatment adherence. Although over half of patients with ESKD were placed on hemodialysis treatments three times per week, less than half adhered to all treatment regimens over the past four weeks. Notably, more than half of patients with ESKD admitted to missing at least one hemodialysis session in the past four weeks. This pattern of nonadherence is consistent with findings from studies conducted in Australia by Ghimire et al. [43] and in Tanzania by Mohamedi and Mosha [44]. In contrast, studies in Saudi Arabia by Alhamad et al. [45] and Alzahrani and Al-Khattabi [46], along with research in Rwanda by Mukakarangwa et al. [47], reported higher hemodialysis adherence rates among patients with ESKD. This discrepancy raises questions about the healthcare infrastructure in Ghana, indicating a need for local interventions such as financial support programs and subsidized dialysis services tailored to the financial and social realities of patients with ESKD in Ghana. Further research is needed to more fully understand the range of barriers affecting the uptake of hemodialysis care in this setting.

The majority of patients in this study encountered significant financial challenges in accessing hemodialysis. Notably, more than half of the participants reported a monthly family income of GHC 1,000 or less, and 20.5% reported having no income at all (GHC 0). These findings suggest that limited financial resources are a major barrier to affording the substantial costs associated with hemodialysis in this population. In contrast, those with higher incomes often have comprehensive health insurance that covers a significant portion of dialysis expenses [48]. This aligns with a multi-facility study in Pennsylvania and New Mexico by Devaraj et al. [49], Hockham et al. [50] in India, Park et al. [51] in Korea, and Agudelo-Botero et al. [41] in Mexico, which also identified financial difficulties as a major barrier preventing patients with ESKD from receiving hemodialysis. Although previous research indicates financial constraints as a significant barrier, it is crucial to address disparities in hemodialysis treatment access for patients with ESKD, particularly those with lower incomes. Several strategies can be implemented to alleviate financial burdens and make treatment more accessible [52, 53].

Over half of patients in this study reported they sometimes went without the dialysis care they needed because it was too expensive. 36% of patients reported that someone occasionally gave them money when they needed it to help pay for dialysis. As Ghana has no tax-based health insurance system that pays for dialysis, nor a government social safety net, patients typically relied on informal sources to fund their healthcare, such as immediate and extended family members, close friends, churches, and philanthropists. This informal support network plays a crucial role in helping patients access and continue treatment. While these support networks are willing to assist, especially in the initial stages of treatment, they often become financially constrained over time, making it difficult to continue providing financial help. This finding is in agreement with a study in Uganda by Ogwang et al. [54], where patients with ESKD received some level of family financial support or were sponsored by family members, especially when needed. However, contrasting evidence from Ghana [55, 56] highlighted that many patients did not receive the support they desperately needed. This divergence illustrates the fragile nature of informal support systems; helpful in the short term but ultimately unreliable for sustaining long-term dialysis care.

The study results also revealed significant disparities in access to healthcare services between patients with ESKD at TTH and KBTH. Patients at KBTH, which is in Southern Ghana, had greater access to healthcare services compared to those at TTH (Northern Ghana), a notable finding in this study. This outcome reflects the broader, well-documented disparities in healthcare infrastructure and economic development between the southern and northern regions of Ghana, where higher levels of poverty have been historically reported. This is consistent with research conducted by Tannor and Antwi [57] and Tuoyire et al. [58] in Ghana, who pointed to resource gaps in rural areas and support the broader understanding of healthcare inequality observed in studies by Harrison et al. [59] and Ashrafi et al. [60] in the United States. To mitigate this disparity, it is crucial to implement targeted interventions such as increasing healthcare infrastructure investment in underserved regions, expanding mobile dialysis units, and enhancing telemedicine services to bridge the gap in access to care for patients with ESKD in Northern Ghana.

The current study results indicate that many patients with ESKD had to travel long distances to access hemodialysis, both in the north (TTH) and the south (KBTH). Although no statistically significant difference was observed between the two sites for distance-related barriers (p = 0.626), the mean scores at both sites suggest that travel remains a challenge for many patients. This finding is in accordance with previous research [61, 62], which revealed that patients living in rural areas face more barriers in accessing equitable care, such as hemodialysis, and tend to experience worse outcomes compared to those in urban centers. The geographical distribution of dialysis services is highly uneven, with many regions lacking any dialysis facilities in Ghana. Urban areas typically have more healthcare resources, including trained personnel and dialysis machines [57]. Therefore, building additional dialysis centers in rural areas can help reduce travel time and make treatment more accessible. Additionally, utilizing remote monitoring technologies can help healthcare providers track the health status of patients with ESKD and make timely interventions when necessary [63].

Similar to previous research findings in Ghana [64], in Egypt [65], and in Greece [66], the functional well-being of patients with ESKD was significantly impacted, with many experiencing disruptions in social activities and daily functioning due to physical and emotional challenges. A striking finding from the current study is that most participants reported that their poor health consistently prevented them from working, doing housework, or attending school. This finding is consistent with studies in the Netherlands by Alma et al. [67], Indiana by Hallab and Wish [68], and Zambia by Kasonde et al. [69], which reported that loss of employment is a common consequence of ESKD among working-age individuals, negatively impacting their ability to participate in work and sustain employment [70].

In addition to the challenges faced by patients with ESKD, the impact of their condition extends to their families. Low et al. [71] emphasized the disruptive influence of dialysis treatments on the social lives of family members. Ziegert [72] further reported that families often experience feelings of confinement, social isolation, and fear of the patient’s death as part of their everyday life with a relative on hemodialysis. These findings, along with the current study, suggest that interventions such as mental health and emotional support should not only focus on patients with ESKD but also include their support networks. Educating patients and their families on the importance of social support, providing strategies to maintain social connections, and encouraging community involvement may help alleviate the social and emotional burdens of ESKD.

Strengths and limitations

This study has several strengths. Notably, it was conducted across two geographically and socioeconomically distinct regions of Ghana, enhancing sample diversity and providing broader insight into the experiences of patients with ESKD. The inclusion of both patients currently undergoing hemodialysis and those not receiving dialysis allowed for a more comprehensive understanding of healthcare service and social support needs within this population.

However, the study also has limitations. The sample size of 264 participants may not fully capture the experiences of all individuals with ESKD in the selected regions, which could limit the generalizability of the findings. Importantly, the study is based on two hospital sites and therefore reflects site-specific experiences rather than national patterns. Furthermore, the use of self-reported data may introduce recall or social desirability bias. Additionally, the cross-sectional design limits the ability to draw causal inferences. Lastly, although the study addressed social support needs, it did not explore how different types of social support such as emotional, financial, and instrumental support affect patients’ health outcomes and hemodialysis utilization.

Conclusion

The study found that the most commonly reported cause of ESKD among patients, based on participants’ self-reports of what they had been told, was hypertension, and most had been managing the condition for approximately three months to one year. Adherence to prescribed treatment was notably low, with fewer than half of the patients following their regimen over the past four weeks. Additionally, a substantial proportion of patients missed treatment sessions in the last week, often due to financial constraints and the distance to healthcare facilities. The study also revealed notable disparities in healthcare access between patients at TTH and KBTH, with nearly half of the patients reporting difficulties in accessing emergency care.

Implications and future research needs

The study underscores the importance of considering sociodemographic factors in understanding and addressing the healthcare services and social care needs of patients with ESKD. The varied healthcare services and social care needs pose challenges in accessing and receiving hemodialysis treatment, highlighting the need for support systems for patients undergoing hemodialysis. Tailored interventions that account for income levels, social support, dialysis frequency, transportation modes, and travel distances are essential to ensure equitable and effective healthcare delivery.

There is a need for comprehensive interventions, including healthcare financing strategies, infrastructure improvements, and strengthened social support systems, to address the challenges faced by patients with ESKD. Future research should build on these findings using larger, multi-site or nationally representative samples to better characterize geographic variation in dialysis access across Ghana. Future research should also explore the effectiveness of these targeted interventions and investigate how different types of support (financial, emotional, and instrumental) interact to influence health outcomes and dialysis adherence. Additionally, longitudinal studies are needed to examine how patient experiences and barriers evolve over time, and to assess the long-term impact of context-specific policy and clinical initiatives aimed at improving access to hemodialysis care.

Abbreviations

CKD

Chronic Kidney Disease

ESKD

End-Stage Kidney Disease

ESRD-AQ

End-Stage Renal Disease Adherence Questionnaire

KBTH

Korle-Bu Teaching Hospital'

SD

Standard Deviation

TTH

Tamale Teaching Hospital

Author contributions

Milipaak Japiong (MJ): Study conception and design, acquisition of data, analysis and interpretation of data, drafting of the manuscript.- Christine Kurtz Landy (CKL): Study design, acquisition of data, analysis and interpretation of data, drafting of the manuscript, critical review of the manuscript.- Mary T. Fox (MTF): Study design, acquisition of data, analysis and interpretation of data, drafting of the manuscript, critical review of the manuscript.- Joseph Mensah (JM): Study design, acquisition of data, analysis and interpretation of data, drafting of the manuscript, critical review of the manuscript.- Peter Adatara (PA): Study design, acquisition of data, analysis and interpretation of data, drafting of the manuscript, critical review of the manuscript.- Mudasir Mohammed Ibrahim (MMI): Acquisition of data, analysis and interpretation of data.- All authors read and approved the final manuscript.

Funding

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

Data availability

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethical approval

The study was approved by the Human Participants Review Sub-Committee of York University’s Ethics Review Board in Toronto, Canada (STU 2023-082), and by the Korle Bu Teaching Hospital Scientific and Technical Committee/Institutional Review Board in Ghana (KBTH/IRB/000135/2023). The study was conducted in accordance with the ethical principles of the Declaration of Helsinki (2013) and the Canadian Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS 2, 2022). All participants provided electronic informed consent prior to participating in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.