Magnitude of multimorbidity and associated factors among stroke survivors in public hospitals of Ethiopia

Abstract

Background

Multimorbidity is a growing global challenge, associated with premature death, impaired activities of daily living, reduced capacity for independent living, poor functional outcomes, and lower quality of life. However, there is limited evidence on multimorbidity and their determining factors among stroke survivors in the Ethiopian context. This study aimed to assess the prevalence of multimorbidity and its associated factors among stroke survivors in public hospitals of Amhara Regional State Northwest Ethiopia.

Methods

A multi-center, institution-based cross-sectional study was conducted from June 26 to August 30, 2024. Systematic random sampling was used to select 292 study participants. Data were collected using a structured, interviewer-administered questionnaire and chart review. Bivariable and multivariable logistic regression analyses were performed to identify factors associated with multimorbidity. Variables with a p-value < 0.05 in multivariable analysis were considered statistically significant.

Result

The prevalence of multimorbidity was 72.9%. Hypertension was the most frequently reported comorbidity. Significant factors associated with multimorbidity included age 50 and above (AOR: 2.48, 95% CI: 1.29, 4.74), having no formal education (AOR: 3.72, 95% CI: 1.49, 9.26), secondary education (AOR: 3.76, 95% CI: 1.46, 9.73), use of assistive technology (AOR: 2.60, 95% CI: 1.32, 5.09), duration of hospitalization (AOR:3.08,95%CI:1.37,6.95), and post-stroke disability (AOR: 4.47, 95% CI: 2.23,8.93).

Conclusions

Multimorbidity is highly prevalent. Targeted interventions particular focus on aged population, educational status, assistive technology provision, and post-stroke disability are essential to improve health outcomes.

Keywords

comorbidity multimorbidity stroke disability stroke survivors

Background

Multimorbidity, defined as the coexistence of two or more chronic conditions,¹ is an increasing global public health problem, particularly in low- and middle-income countries (LMICs), including Ethiopia.^2,3 Among people living with chronic diseases, stroke survivors constitute a particularly vulnerable group, as they commonly experience multiple comorbid conditions due to shared risk factors and stroke-related complications. The presence of multimorbidity complicates clinical management, increases treatment burden, and associated with poorer health outcomes and reduced quality of life, largely because of disease-disease and disease-drug interactions.^4,5

In Ethiopia, the burden of stroke is high, with the pooled prevalence of ischemic and hemorrhagic stroke reported as 46.42 and 51.40, respectively,⁶ while access to comprehensive rehabilitation services remains limited. The coexistence of multiple chronic conditions further worsens morbidity and impedes optimal recovery among stroke survivors.⁷ Evidence from high-income countries shows a high magnitude multimorbidity among stroke survivors. Studies from the United kingdom (UK)⁸ and Scotland⁹ report that 85-94% of stroke survivors have at least one additional comorbid condition, nearly double the prevalence observed in the general population. Hypertension (HTN) is the most common comorbidity, affecting over 60% of stroke survivors and is a major contributor to increased treatment burden, reduced adherence to rehabilitation, and poorer post stroke outcomes.^10,11

The relationship between multimorbidity and health outcomes is multi-faceted. Stroke survivors often experience multiple chronic conditions including HTN, diabetes mellitus (DM), heart disease, and mental health disorders. These conditions may coexist before the stroke or develop afterward as secondary complications, both of which affect functional recovery.¹² Post-stroke disability, prolonged hospitalization, dependence on assistive technology can further increase the likelihood of multimorbidity, particularly in settings where healthcare access and continuity of care is limited.^13–15

Multimorbidity has been consistently associated with increased healthcare costs, longer hospital stays, higher mortality risks,^16,17 and reduced health-related quality of life (HRQOL).^18–22 Evidence from Ethiopia shows stroke survivors frequently experience disability and depression, both of which significantly reduce HROQOL and complicates rehabilitation and long-term management.²³ Stroke survivors with multiple chronic conditions often face compounded physical, cognitive, and psychological sequelae that further delay recovery.²⁴ Globally, post-stroke impairment is common, with up to 75 % of stroke survivors experiencing some level of impairment and 15-30% reporting long-term disability.^25,26 Chronic conditions such as DM, musculoskeletal disorder and mental health problems-particularly depression-play significant role in these adverse outcomes.^27–30

In resource-restricted settings like Ethiopia, multimorbidity is influenced by a combination of socioeconomic and clinical factors, including age, educational status, socioeconomic condition, duration of hospitalization, use of assistive technologies, and post-stroke disability.^25,31,32 Older adults, individuals with lower educational attainment, and those with functional limitations are particularly at risk.³² Understanding, how these factors interact to influence multimorbidity is therefore essential for developing effective, individualized, and context-specific care strategies.³³

Given the rising burden of stroke and its frequent coexistence with other chronic conditions,³⁴ understanding the prevalence and associated factors of multimorbidity among stroke survivors is critical. Finding from this study will help inform integrated, patient-centered approaches to stroke care that address both stroke and comorbid conditions, with the potential to improve rehabilitation outcomes and quality of life. Therefore, this study aims to assess prevalence of multimorbidity and its associated factors among stroke survivors attending in public hospitals in the Amhara regional state.

Methods

Study design and period

A multicenter cross-sectional study was conducted from July 1 to August 30, 2024, among stroke survivors attending public hospitals in the Amhara Regional State, Ethiopia.

Study settings

According to the Amhara National Regional Health Bureau, the region has 81 hospitals, 858 health centers, and 3560 health posts. Of the eight comprehensive specialized hospitals providing medical follow-up and physiotherapy services for stroke survivors, five hospitals were randomly selected for this study: University of Gondar Comprehensive Specialized Hospital (UOGCSH), Felege-Hiwot Comprehensive Specialized Hospital (FHCSH), Tibebe Ghion Comprehensive Specialized Hospital (TGCSH), Debre-Markos Comprehensive Specialized Hospital (DMCSH), and Debre-Tabor Comprehensive Specialized Hospitals (DTCSH).

For simplicity, the hospital were labeled A -E (see Table 1). Hospital A is located in Gondar city, approximately 655 km northwest of Addis Ababa. Hospitals B and C located in Bahir Dar, the capital of the Amhara National Regional State, about 486 km from Addis Ababa. Hospital D is found in Debre Markos City, roughly 300 km from Addis Ababa and 250 km from Bahir Dar. Hospital E is located in Debre Tabor city, approximately 100 km from Bahir Dar and about 589 km from Addis Ababa.

Table 1.

Details of hospital, including head count, study sample, and selected sample size in Amhara Regional State, Ethiopia.

Hospital code	Name of hospital	Head count at hospital	Study sample	Sample size
A	UOGCSH	240	69	68
B	FHCSH	210	65	64
C	TGCSH	206	64	61
D	DMCSH	160	50	46
E	DTCSH	190	58	53

Currently, these comprehensive specialized hospitals serve a catchment population of more than 20 million people, including those from neighboring regions.

Study population

The study population consisted of stroke survivors attending medial and physiotherapy outpatient follow-up services at the selected hospitals during the data collection period. Based on hospitals reports, an estimated total of 1,006 stroke survivors attended the medical and physiotherapy outpatient departments during the two-month data collection period.

Inclusion and exclusion criteria

Stroke survivors’ aged 18 years and above, with at least two months since stroke onset, were included. Stroke survivors diagnosed with dementia, severe cognitive impairment, aphasia, or other additional neurological conditions were excluded.

Sample size determination

The sample size was calculated using a single population proportion formula. Since no prior study on this topic has been conducted in Ethiopia, a proportion of 50% was assumed, with a 5% margin of error and a 95% confidence level. A 10 % non-response rate was added to account for incomplete data.

Based on hospitals reports, an estimated total of 1,006 stroke survivors attended the medical and physiotherapy outpatient departments during the two-month data collection period:

The initial sample size was calculated as follows:

n = \frac{{({Za}_{/ 2})}^{2} * P (1 - P)}{d^{2}} = \frac{{(1.96)}^{2} * 0.5 (1 - 0.5)}{{(0.05)}^{2}} = 384.16 \sim 385

Where n=samples, P-prevalence and d-margin of error

Since the total population (N=1006) was less than 10,000, the finite population correction formula was applied.

n^{'} = \frac{n}{1 + \frac{1}{N}} = \frac{385}{1 + \frac{385}{1006}} = 278

After adding a 10 % non-response rate, the final sample size was 306 (278+28) participants.

Sampling strategies

A proportional allocation was used to distribute the sample across the selected hospitals. During the study period, 240, 210, 206, 160, and 190 stroke survivors attended the outpatient departments of hospital A, B, C, D, and E, respectively, totaling 1006 stroke survivors. The sampling interval (K^th) was calculated by dividing the total number of stroke survivors by the final sample size (1006/306=3). The first participant in each hospital was randomly selected using a lottery method and, every third eligible participants was recruited systematic random sampling until the required sample size was reached. Accordingly, 68 participants were selected from A, 64 from B, 61 from C, 46 from D, and 53 from E. See Table 1.

Data collection tool and procedures

Data were collected from complementary sources, including interviewer-administered questionnaire developed from various literature sources,^8,35–38 medical chart reviews, and standardized assessment tools. The questionnaire included sociodemographic and clinical characteristics of the stroke survivors, and sociodemographic information was collected through direct interviews.

Medical chart review served two purposes¹: to extract clinical characteristics of stroke, including stroke type, lesion location, duration since stroke onset, duration of hospitalization, and related medical history; and² to identify physician-diagnosed chronic conditions such as HTN, DM, kidney diseases, heart conditions, HIV/AIDS and asthma. These conditions were used to construct the outcome variables-multimorbidity-defined as the coexistence of two or more chronic conditions.

Functional and psychological status of stroke survivors were used validate tools. Post-stroke disability was assessed using the 12-item World health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) while the Hospital Anxiety and Depression Scale (HDAS) tools was used assess depressive and anxiety symptoms among stroke survivors. Both the WHODAS 2.0^39,40 and the HDAS⁴¹ are acceptable, validated and reliable tools for Amharic speakers in Ethiopia.

Definitions of variables

Multimorbidity

The co-existence of two or more physician-diagnosed comorbid conditions in stroke survivors.^42,43 A chart review was done to identify physician-diagnosed comorbid conditions.

Depression and anxiety disorder

Post-stroke depression and anxiety are measured by the Hospital Anxiety and Depression Scale (HADS). It has a total score of 0-42 (0-21 for each subscale (depression and anxiety). A score of ≤ 7 on either the depression or anxiety subscale is considered being in the normal range (HADS-D ≥8) HADS-A ≥8.⁴⁴

Data quality assurance

The questionnaire was initially prepared in English, then translated into Amharic, the local language, to ensure clarity and culturally appropriateness. The translation was independently done by two physiotherapy lectures who hold master’s degrees and experience in research and clinical practice. Discrepancies were resolved through consensus to maintain conceptual and semantic equivalence with the original English version. The Amharic questionnaire was then pretested on a pilot study of 5% of stroke survivors to assess clarity, comprehension, and applicability, and necessary revisions were made accordingly.

Data were collected by five physiotherapists, with one data collector assigned to each hospital. Prior to data collection, a one-day training was provided by the supervisor, covering the study objectives’, participant approach, informed consent procedures, use of the data collection tool, and data collection procedures. During data collection, continues supervision and daily checks were conducted by the supervisor and principal investigator to ensure completeness, accuracy, consistency, and adherence to the study protocol.

After data collection, completed questionnaires were reviewed for completeness and consistency before data entry to maintain data quality.

Data analysis

The collected data were coded and entered into EPI-data version 4.4.3.1 and then exported to SPSS version 23 for analysis. The normality of continuous variables was assessed using the Shapiro-Wilk test. Variables with a p-value <2.0 in the bivariable logistic regression analysis were considered candidates for the multivariable logistic regression model. Multicollinearity among independent variables was assessed using the variance inflation factor (VIF), with values < 10 indicating the absence of significant multicollinearity. Model fitness was evaluated using the Hosmer-Lemeshow goodness-of-fit test, which indicated an adequate model fit (p-value=0.49).

Descriptive statistics, including means, frequencies, percentages, and tables, were used summarizes the characteristics of the study participants. Binary logistic regression analysis was performed to identify factors associated with multimorbidity among stroke survivors. Crude odds ratios (COR) and adjusted odds ratios (AOR), with 95% confidence intervals (95% CI), were calculated to estimate the strength of associations between the dependent and independent variables. Variables with a p-value <0.05 in the multivariable analysis were considered statistically significant.

Results

A total of 292 stroke survivors participated in this study, with a response rate of 95.4%. The remaining participants were excluded due to incomplete data. The finding includes the sociodemographic characteristics, clinical information, prevalence of multimorbidity, and factors associated with multimorbidity among stroke survivors in public hospitals of the Amhara Regional State, Ethiopia.

Prevalence of multimorbidity among stroke survivors in public hospitals, Ethiopia

The prevalence of multimorbidity is 72.9 % (213) (95 % CI: 68.7%-78.1%).

Sociodemographic characteristics of stroke survivors

A total of 292 stroke survivors participated in this study, with mean age of 56.3±14.2 years and a response rate of 95.4%. More than half of the participants were male. Nearly two-thirds were married and resided in urban areas. About one-third of the participants had no formal education, and over two-thirds reported a monthly income less than ETB 5000. See Table 2.

Table 2.

Sociodemographic characteristics of stroke survivors in public hospitals, Ethiopia, 2024.

Variables	Category	Frequency (%)
Age	50 and below	121(41.4%)
Age	Above 50	171(58.6)
Sex	Male	164(56.2)
Sex	Female	128(43.8)
Marital status	Married	202 (69.2)
Marital status	Unmarried	90(20.8)
Residence	Urban	192(65.8)
Residence	Rural	100(34.3)
Educational level	Have no formal education	87(29.8)
	Primary education	72(24.7)
	Secondary education	52(17.8)
	Diploma and above	81(27.7)
Occupation	Governmental employee	80(27.4)
	Housewife	71(24.3)
	Farmer	56 (19.2)
	Non-governmental employee	55(18.8)
	Retire	30(10.3)
Income	<5000	205(70.2)
Income	>5000	87(29.8)

Clinical factors among stroke survivors in public hospitals, Ethiopia

More than half of the study participants experienced an ischemic type of stroke (59.6%), with left side involvement (52.7%). Additionally, 53.8% were dependent on assistive devices for mobility. See Table 3.

Table 3.

Clinical factors among stroke survivors in public hospitals, Ethiopia, 2024.

Variables	Categories	Frequency (%)
Type of stroke	Ischemic	174(59.6)
Type of stroke	Hemorrhagic	118(40.4)
Involved side	Right side	138(47.3)
Involved side	Left side	154(52.7)
Usage of AD	Yes	157 (53.8)
Usage of AD	No	135(46.2)
Duration of hospitalization	Short stay (<7 days)	63(21.6)
	Moderate (7-14 days)	99 (33.9)
	Long (>14 days)	130 (44.5)
Post-stroke disability	Yes	144 (49.3)
Post-stroke disability	No	148(50.7)

Factors associated with multimorbidity among stroke survivors in public hospitals, Ethiopia

Age above 50, low educational attainment, usage of assistive devices, prolonged hospitalization, and having a disability were significantly associated with multimorbidity. See Table 4.

Table 4.

Factors associated with multimorbidity among stroke survivors in public hospitals, Ethiopia, 2024.

Variables	Categories	Multimorbidity		COR 95% CI	AOR %CI	p-value
Variables	Categories	Yes	No	COR 95% CI	AOR %CI	p-value
Age	50 and below	70 (57.9)	51(42.1)	Ref	Ref
Age	Above 50	143(83.6)	28(16.3)	3.73(2.16, 6.40)	2.48(1.29,4.74)	0.006
Sex	Male	110(67.1)	54(32.9)	Ref	Ref
Sex	Female	103(80.5)	25(19.5)	2.02(1.17, 3.45)	1.54(0.77, 3.06)	0.22
Marital status	Married	142(70.3)	60(29.7)	Ref	Ref
Marital status	Unmarried	71(78.9)	19(21.1)	1.58(0.87, 2.84	1.18(0.54, 2.41)	0.64
Education	Have no formal education	74(85.1)	13(14.9)	5.28(2.53, 11.00)	3.72(1.49,9.26)	0.005
	Primary education	54(75)	18(25)	2.78(1.39,5.54)	1.87(.082,4.22)	0.13
	Secondary education	43(82.7)	9(17.3)	4.43(1.91, 10.28)	3.76(1.45, 9.73)	0.006
	Diploma and above	42(51.9)	39(48.1)	Ref	Ref
Occupation	Govern. Employ	50(62.5)	30(17.5)	Ref	Ref
	Housewife	60(84.5)	11(15.5)	3.27(1.49, 7.18)	1.15(0.303, 4.39)	0.83
	Farmer	45(80.4)	11(19.6)	2.45(1.10, 5.46)	0.61(0.11, 3.20)	0.55
	Non-governmental employee	34(61.8)	21(38.2)	0.97(0.47, 1.97)	0.73(0.29, 1.80)	0.49
	Retired	24(80)	6(20)	2.40(.88, 6.54)	1.05(0.28, 3.87)	0.94
Involved side	Right side	109(79)	29(21)	1.81(1.06, 3.07)	1.85(0.969, 3.52)	0.06
Involved side	Left side	104(67.5)	50(32.5)
Usage of AD	Yes	130(82.8)	27(17.2)	3.02 (1.75, 5.17)	2.60(1.32,5.09)	0.005
Usage of AD	No	83(61.5)	52(38.5)
Duration of hospitalization in days	Short stay (<7 days)	33(52.4)	30(47.6)	Ref	Ref
	Moderate (7-14)	72(72.7)	27(27.3)	2.42(1.24, 4.70)	1.69(0.76, 3.77)	0.19
	Long (14 and longer)	108(83.1)	22(16.9)	4.46(2.27, 8.75)	3.08(1.37, 6.95)	0.006
Post-stroke disability	Yes	128(88.9)	16(11.1)	5.93(3.21,10.95)	4.47(2.23, 8.93)	0.000
Post-stroke disability	No	85(57.4)	63(42.6)	Ref	Ref

Discussion

Our study assessed the prevalence of multimorbidity and its associated factors among stroke survivors in public hospitals in Ethiopia. We found a high prevalence, with nearly three-quarters (72.9%) of participants experiencing two or more comorbid conditions. This aligns from rural Appalachia, USA, where 78% of stroke survivors had three or more comorbid conditions.³⁸ However, our prevalence was lower than that reported in the UK (85%)⁸ and the USA (90%).³⁵ These differences may reflect variations in life expectancy, healthcare access, and socioeconomic conditions between high- and low-income countries. For instance, the mean age of participants in the USA³⁵ and UK⁸ studies was 78 and 60.9 years, respectively, compared to 56.2 years in our study. Stroke survivors in high-income countries tend to live longer, increasing the likelihood of accumulating chronic conditions.³⁶ Socioeconomic disparities, limited preventive care, and lower diagnostic capacity in low income-settings may also contribute to the observed differences.⁴³

Conversely, our prevalence was higher than the 27.9 % reported in a UK population-based study,³⁷ likely due to difference in study design and study participant characteristics. Population-based studies typically include healthier individuals, whereas our facility-based study included stroke survivors attending medical and physiotherapy follow-up clinics, who are more likely to present with multiple chronic conditions.

In our study, depressive symptoms, and HTN were the most frequently reported comorbidities, accounting for 72.9% and 66.1% of stroke cases, respectively. These findings are consistent with prior Ethiopian studies, which reported prevalence rate of 64.1%⁴⁵ and 47%⁴⁶ for depressive symptoms and 33.5% for HTN.⁴⁵ A similar pattern has been observed globally, with HTN reported in 50.8%⁴⁷ and 92.6%⁴⁸ of stroke survivors in Nigeria, 54%-84% in the USA(35, 39), and 77 %⁴⁹ to 80 %⁵⁰ in Sweden and Scotland respectively. This findings underscores the global role of HTN as a leading risk factor for stroke and recurrent cardiovascular events.

Sociodemographic and clinical factors were significantly associated with multimorbidity. Older age (≥50 years), low educational attainment, use of assistive devices, prolonged hospitalization, and post-stroke disability were significant predictors of multimorbidity. Stroke survivors aged ≥ 50 years had 2.48 times higher odds of multimorbidity (AOR: 2.48, 95% CI: 1.29- 4.74), consistent with findings from the UK, which demonstrated a strong association between age and multimorbidity.⁸ The accumulation of chronic conditions with aging likely explains this association, as cardiovascular disease remains a leading cause of morbidity and mortality among older stroke survivors.⁵¹

Educational status was another factor significantly associated: stroke survivors with no formal education (AOR: 3.72, 95% CI: 1.49-9.26) or secondary education (AOR: 3.76, 95% CI: 1.45-9.73) were more likely to experience multimorbidity compared to those with a diploma or higher. This finding is supported by studies from the USA,⁵² UK,⁵³ and Denmark.⁵⁴ A possible explanation is that lower educational status may reduce health literacy, delay diagnosis, and restrict access to healthcare resources, thereby increasing vulnerability to comorbid conditions.

Among clinical factors, the use of assistive devices was associated with higher odds of multimorbidity (AOR: 2.6, 95% CI: 1.32-5.09), likely reflecting stroke severity and functional limitations. Prolonged hospitalization (>14 days) was also significantly associated (AOR; 3.08, 95% CI 1.37- 6.95) with a higher likelihood of multimorbidity compared to those hospitalized for less than 7 days, consistent with findings from the USA.³⁵ Prolonged hospitalization may reflect complications, slower recovery, deconditioning, increased dependency, and higher healthcare needs due to comorbid conditions.

Post-stroke disability showed a strong association with multimorbidity (AOR: 4.47, 95% CI, 2.23-8.93). This bidirectional relationship indicates that multimorbidity accelerates functional decline, while post-stroke disability increases vulnerability to additional chronic conditions.^55,56 Previous studies have shown that each additional chronic conditions increases the risk of functional impairment by 16%, reduces life expectancy by nearly two years, and raises the need for professional care by 20%.^57,58 Similarly, studies in China)⁵⁹ and Japan⁶⁰ found that multimorbidity was associated with limitations in both activities of daily living (ADL) and instrument activities of daily living (IADL). This finding also supported by a study from USA, which reported that older stroke survivors with multimorbidity experienced greater difficulties with ADLs such as dressing, walking, bathing, toileting, earing and bed mobility.⁶¹

The high prevalence of multimorbidity has important clinical and health-system level implications. Stroke survivors with multiple comorbidities often experience reduced QOL, greater functional limitations and higher risks of recurrent hospitalization. In resource-restricted settings like Ethiopia, this places an additional burden on the healthcare system and emphasizes the need for integrated chronic disease management and post-stroke rehabilitation pathways.

Given that depressive symptoms and HTN are modified risk factors for stroke, strengthening primacy prevention is essential. Community-based screening, lifestyle modification programs promoting physical activity, healthy diet, and smoking cessation, along with adherence to antihypertensive and diabetes treatments, are critical strategies. Integrating preventive and rehabilitative care into primary healthcare systems, supported by public awareness campaigns, may help reduce both initial and recurrent stroke.

Clinically, routine screening for multimorbidity, particularly depression and HTN, should be integrated in to stroke rehabilitation programs. Multidisciplinary care involving neurologists, physiotherapists, and primary care providers is essential. Targeted interventions for high risks groups such as older adults and individuals with disabilities, should focus on improving health literacy, managing comorbidities, and enhancing access to rehabilitation services. Integrating physiotherapy, mental health support, and comorbidity management can help break the disability-multimorbidity cycle.

Strength and limitations

A key strength of this study is that it provides valuable insight into the prevalence and associated factors of multimorbidity among stroke survivors in Ethiopia. These findings offer valuable baseline data to inform clinical practice and guide future research in similar low-resource settings. However, the cross-sectional design limits causal interference between identified factors and multimorbidity. In addition, the study sample may not be fully representative of all stroke survivors, as recruitment was restricted to government hospitals and individuals attending scheduled follow-up visits. This approach may have excluded stroke survivors receiving care in private facilities or those without regular access to follow-up services, potentially introducing selection bias related to healthcare access and socioeconomic status. Moreover, the hospital-based nature of the study may have excluded community-dwelling stroke survivors, which could have led to an underestimation of the true prevalence of multimorbidity. Future longitudinal, population-based studies are warranted to establish temporal relationships and better elucidate the mechanisms underlying the identified determinants.

Conclusion

In conclusion, the high prevalence of multimorbidity among stroke survivors, coupled with its strong association with age, educational attainment, prolonged hospitalization, utilization of assistive devices, and post-stroke disability, highlight the need for integrated, patient-centered care. Addressing the identified predictors through individualized, multidisciplinary interventions can help reduce the burden of multimorbidity and improve the QOL for stroke survivors. Future research should include larger, community-based samples from both public and private facilities to enhance generalizability.

Footnotes

Author note

¹Department of physiotherapy, College of Medicine and Health Science, University of Gondar, Gondar Ethiopia.

²Department of Physiotherapy, University of Gondar Comprehensive Specialized Hospital, University of Gondar, Gondar Ethiopia.

Acknowledgments

Firstly, we would like to express our deepest gratitude to the University of Gondar for funding this work. Our gratitude and appreciation go to the data collectors, study participants, neurologic and physiotherapy OPD staff, and data collectors.

ORCID iD

Getachew Azeze Eriku

Ethical considerations

Ethical clearance was obtained from the Ethical Review Committee of the School of Medicine College of Medicine and Health Sciences, University of Gondar (Ref No.1558/2024), in accordance with the principles of the declaration of Helsinki.⁶²

Consent to participate

All participants provided a written informed consent to participate in the study.

Consent for publication

Consent for publication section was to indicate that there is no personal information or photos of participants that would require consent for publication.

Authors’ contributions

GAE and CM were responsible for designing the study, data acquisition, conceiving the study, dataset tabulation, and data analyses, and supervised the project. TK, MDT, DMM, and ESY participated in the analysis, manuscript writing, and revised the draft. All authors reviewed and approved the final manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was fully resourced and funded by the University of Gondar. The views presented in the article are the authors and not necessarily express the views of the funding organization. University of Gondar did not involve in the design of the study, data collection, analysis, and interpretation.

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 Statement

All data relevant to our findings are contained within the manuscript. Requests for further details on the dataset and queries concerning data sharing shall be arranged based on a reasonable request to the first and corresponding author (Getachew Azeze Eriku).*

Appendix

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