Impact of non-motor symptoms on the quality of life of Parkinson’s disease patients in a cohort of Senegalese patients

Abstract

Background

Non-motor symptoms (NMS) are common in Parkinson’s disease (PD). They contribute significantly to changes in patients’ quality of life (QOL).

Objective

To assess the impact of NMS on the QOL of patients with PD in Senegal.

Methods

We conducted a cross-sectional study from January to August 2024. All patients who met the diagnostic criteria for PD and were followed up in the neurology department at the Pikine National Hospital Center were included. NMS were assessed using the Movement Disorders Society-Non-motor Symptoms Scale (MDS-NMS), while QOL was assessed using the 39-item Parkinson’s Disease Questionnaire (PDQ-39). The Hoehn and Yahr scale was used to determine the disease stage for each patient. The student’s t-test was used to compare quantitative variables, and linear regression was used to identify predictive factors for poor QOL.

Results

A total of 52 patients with PD were enrolled. The mean age was 64.6 ± 10 years. The mean MDS-NMS score was 61.8 ± 30.48. The most prevalent NMS were sleep disturbance (98%), constipation (84.6%), and pain (84.6%). The mean PDQ-39 index was 23.45 ± 12.5%. The most affected domains for the QOL were mobility and activities of daily living. There was a significant correlation between the MDS-NMS score and the PDQ-39 index (β = 0.448; P = 0.001). In addition, depression (β = 0.398; P = 0.043) and cognitive impairment (β = 0.355; P = 0.046) were predictive of poor QOL. Patients presenting with depression (p = 0.000), fatigue (p = 0.018), urinary urgency (p = 0.03), and hypersalivation (p = 0.003) had a significantly worse QOL than those who did not.

Conclusion

In our cohort, non-motor symptoms, particularly depression and cognitive impairment, were significantly associated with poorer quality of life. Although the confounding effect of motor symptoms was not fully assessed, motor severity, assessed by the Hoehn and Yahr stage, also contributed substantially.

Introduction

Parkinson’s disease (PD) is the 2nd most common neurodegenerative disease in the world after Alzheimer’s disease [1]. In sub-Saharan Africa, the prevalence of idiopathic Parkinson’s disease (IPD) varies between 7–67/100,000 inhabitants [2, 3]. Diagnosis of IPD is based primarily on motor symptoms (resting tremor, akinesia, rigidity, postural instability). However, non-motor symptoms are often present in IPD and may precede the appearance of motor signs by several years. The most common non-motor symptoms of IPD are sleep disorders, anxiety disorders, autonomic dysfunction, and cognitive disorders [4]. These non-motor symptoms may dominate the clinical picture and may influence the quality of life of people with IPD [5].

Several studies have reported that non-motor symptoms significantly contribute to the deterioration in quality of life of patients with IPD [4, 6, 7]. This relationship has been robustly established in seminal studies, including the PRIAMO study and Martinez-Martin et al., which demonstrated that non-motor symptoms are major determinants of health-related quality of life in PD, often exceeding the impact of motor symptoms [8]. In sub-Saharan Africa, the data on the non-motor signs of IPD are scanty, and the impact of these symptoms on the quality of life of IPD patients has hardly been studied [9–12]. Furthermore, emerging evidence suggests that ethnicity and geographic background may influence disease phenotype and access to care, highlighting the need for context-specific data from underrepresented populations. A study by Lau et al. and the EmPark study have emphasized disparities in representation and clinical characterization across populations [13, 14]. Given that IPD is a chronic disease, improving the quality of life of IPD patients is essential for the daily functioning and long-term survival of these patients. In this context, data from African populations remain particularly limited. Given the paucity of literature on the subject in our context, and to improve the management of patients with IPD in sub-Saharan Africa in general and in Senegal in particular, we considered it relevant to conduct a cross-sectional study to assess the impact of non-motor symptoms on the quality of life of patients with IPD.

Methods

Study design

The study was conducted in the neurology department at the National Hospital Center in Pikine, Senegal. This was a cross-sectional, descriptive, and analytical study conducted between January and August 2024.

Study participants

We included patients who met the criteria for IPD, were being followed in the neurology department of the National Hospital Center at Pikine during the study period, and had given informed consent to participate in the study. A patient was diagnosed as IPD if they met the criteria proposed by the Movement Disorder Society (MDS Clinical Diagnostic Criteria for Parkinson’s Disease) [15]. Patients who were diagnosed with atypical Parkinsonism or secondary Parkinsonism were excluded.

Assessments

Data were collected using forms developed on the Kobocollect and Redcap digital data collection platforms. We collected both socio-demographic and clinical data.

Non-motor signs of IPD were collected using the MDS-NMS scale after obtaining authorization to use it from the Movement Disorder Society [16]. The MDS has validated this scale, which is the most frequently used in the literature. The MDS-NMS scale comprises 52 items categorized into 13 domains of non-motor symptoms related to Parkinson’s disease and antiparkinsonian treatment. The scale also includes an MDS-NMS Non-Motor Fluctuations (NMF) subscale to assess changes in non-motor symptoms across 8 domains in relation to the timing of antiparkinsonian medication. Patients’ quality of life was assessed using the PDQ-39 scale. The PDQ-39 is a quality-of-life assessment scale that is specific to Parkinson’s disease [17]. This is the most widely used scale to assess the quality of life of Parkinson’s disease. It consists of 39 items which assess 8 dimensions of daily life. Total scores range from 0 to 100, with higher scores indicating poorer quality of life. A summary index (PDQ_SI) was calculated as the average of the 8 domain scores. The Hoehn and Yahr scale was used to stage motor severity in PD.

Statistical analysis

Qualitative variables were expressed as frequencies and percentages, while quantitative variables were expressed as means and standard deviations. Quantitative variables were compared using Student’s t-test. In addition, we used linear regression analysis to determine which non-motor signs were most predictive of poor quality of life. For these tests, a p-value of less than 0.05 was considered statistically significant. Statistical analyses were performed using SPSS version 26 software.

Results

Descriptive statistics

A total of 52 patients were enrolled. Men were most frequent (83.7%). The mean age was 64.58 ± 10 years, and most patients were between 60 and 80 years. Age of disease onset was less than 40 years for 9.6%. The median duration of Parkinson’s disease was 5 years. The mean age of onset was 59.6 ± 11.7 years. The majority of patients were at stage 2.5 (34.6%) and 2 (19.2%) of Parkinson’s disease according to the Hoehn and Yahr scale. At the time of evaluation, 60% of patients were in the OFF phase. Table 1 summarises the characteristics of the participants.

Table 1.

Characteristics of Parkinson’s patients

Variables (N = 52)	PD patients (n)	Prevalence (%)
Female	09	17,3
Male	43	82,7
Sex-ratio	5,4
Age (years)	64,58 ± 9,92
Age at onset of IPD	59,6 ± 11,7
Median duration of IPD (years)	5
<40	5		9,6
40–49	4		7,7
50–59	14		26,9
60–70	20		38,5
70	7		13,5
Hoehn & Yahr (Median)	2,5
≤ 2,5	39		75
2,5	13		25
OFF Phase (%)	60

The mean MDS-NMS score was 61.8 ± 30.48. One in two patients had an MDS-NMS score between 40.3 and 83.5. The most prevalent non-motor signs were sleep disorders (98%), constipation (84.6%), pain (84.6%), urinary disorders (78.8%), weight loss (78.8%), and apathy (71.1%). The prevalence of each non-motor sign is listed in Table 2.

Table 2.

Prevalence of non-motor signs of MPI based on MDS-NMS score

MDS-NMS (N = 52)	Prevalence (%)	Mean	SD	Min-Max
A. Depression	32 (61,5)	5,13	7,31	0–28
B. Anxiety	35 (67,3)	3,96	4,38	0–17
C. Apathy	37 (71,15)	2,98	3,07	0–11
D. Psychosis/hallucination	19 (36,5)	1,38	2,96	0–13
E. Obsessive-compulsive disorder	6 (11,5)	0,4	1,51	0–9
F. Cognitive disorders	45 (86,5)	5,31	5,38	0–26
Impaired episodic memory	23 (44,2)	1,33	2,18	0–9
Impaired working memory	27 (51,9)	1,27	1,7	0–6
Attention deficit disorders	17 (32,7)	0,67	1,28	0–4
Speech expression disorders	28 (53,8)	1,38	1,82	0–6
Impaired executive function	6 (11,5)	0,35	1,08	0–4
G. Orthostatic hypotension	30 (57,7)	3,31	5,31	0–32
H. Urinary problems	41 (78,8)	6,65	6,34	0–27
Urinary urgency	33 (63,5)	2,79	2,84	0–9
Pollakiuria	25 (48)	1,38	2,33	0–9
Nocturia	28 (53,8)	1,15	1,27	0–4
I. Sexual dysfunction	34 (65,4)	4,56	4,93	0–18
Decreased libido	24 (46,15)	1,37	2,17	0–9
Erectile dysfunction	27 (51,9)	3,19	3,89	0–12
J. Gastrointestinal disorders	46 (88,5)	7,52	6,13	0–26
Hypersalivation	27 (51,9)	1,94	2,69	0–9
Dysphagia	08 (15,4)	0,67	2,45	0–12
Nausea	10 (19,2)	0,33	0,86	0–4
Constipation	44 (84,6)	4,58	4,28	0–16
K. Sleep disorders/alertness	51 (98)	8,69	5,37	0–22
Insomnia	19 (36,5)	1,58	2,75	0–9
RBD	40 (76,9)	2,48	2,93	0–16
Daytime sleepiness	35 (67,3)	2,08	2,41	0–9
Restless leg syndrome	22 (42,3)	1,10	1,68	0–6
Sleep apnea	04 (7,7)	0,06	0,24	0–1
L. Pain	44 (84,6)	4,04	3,11	0–12
M. Others	51 (98)	7,87	6,51	0–26
Weight loss	41 (78,8)	2,21	2,18	0–9
Anosmia/hyposmia	21 (40,4)	3,03	5,58	0–16
Physical fatigue	31 (59,6)	1,48	2,12	0–9
Mental fatigue	15 (28,8)	0,42	0,87	0–4
Excessive sweating	11 (21,1)	0,52	1,26	0–6
MDS-NMS total	52 (100)	61,81	30,48	7–125
Fluctuation MDS-NMS	16 (30,8)

RBD REM sleep behavior disorder

Only 30.8% (16) reported fluctuation in one or more non-motor signs. Fluctuations in non-motor signs were mainly in urinary disorders (34.1%), excessive sweating (36.4%), sleep disorders (25.5%), fatigue (22.6%), and pain (20.45%) (Table 3).

Table 3.

Assessment of fluctuations of non-motor signs

MDS-NMS fluctuations (N = 16)	Prevalence (%)	Mean	SD	Min-Max
Depression	1 (3,1)	0,11	0,47	0–2
Anxiety	1 (2,9)	0,11	0,47	0–2
Cognitive disorders	7 (15,6)	0,5	0,71	0–2
Urinary disorders	14 (34,1)	1,33	0,84	0–2
Sleep disorders	13 (25,5)	1,28	1,02	0–3
Pain	9 (20,45)	0,72	0,83	0–2
Fatigue	7 (22,6)	0,56	0,78	0–2
Excessive sweating	4 (36,4)	0,28	0,57	0–2
MDS-MNS subscore	16 (30,8)	4,89	3,08	2–16

The mean PDQ-39 score was 23.45 ± 12.5%. 50% (50%) of patients had a PDQ-39 score between 15.8 and 31%. The most affected domains of quality of life were mobility (mean: 27.5 ± 23.7%) and activities of daily living (mean: 30 ± 24.3%). (Table 4).

Table 4.

Quality of life scores based on PDQ-39

PDQ-39 (N = 52)	Mean	SD	Min-Max
Mobility	27,5	24,3	0–97,5
Daily activities	30	23,7	0–87,5
Emotional well-being	18,8	18,6	0–87,5
Psychological disability	20,45	23,8	0–68,8
Social support	2,7	6,75	0–25
Cognitive impairment	31,5	16,2	0–75
Communication	18,1	20,3	0–66,7
Physical discomfort	25,3	17,2	0–66,7
PDQ-39 index	23,45	12,5	2,6–61,1

Analytical statistics

When we compared the PDQ-39 scores of patients with and without specific non-motor signs, patients with depression (P < 0.000), urinary urgency (P = 0.03), hypersalivation (P = 0.003), and physical fatigue (P = 0.018) had significantly higher PDQ-39 scores than those without these symptoms (Table 5).

Table 5.

Comparison of the PDQ-39 score based on the presence or absence of non-motor signs

PDQ-39 (N = 43)	No symptoms (n)		With symptoms (n)		P-value
Depression	16,3 ± 6,6	(15)	29,8 ± 12,5	(28)	0,000
Anxiety	19,7 ± 11,7	(12)	27,2 ± 12,4	(31)	0,076
Apathy	15 ± 6	(12)	29 ± 12,2	(31)	0,000
Psychosis/hallucination	24 ± 13	(27)	27 ± 11,8	(16)	0,453
OCD	24 ± 11,5	(38)	33,6 ± 18	(5)	0,107
Cognitive impairment	22,2 ± 22,1	(5)	25,5 ± 11,1	(38)	0,590
Orthostatic hypotension	21,4 ± 13,4	(17)	27,5 ± 11,6	(26)	0,119
Urinary urgency	19,5 ± 11,4	(15)	28,1 ± 12,3	(28)	0,03
Sexual dysfunction	23 ± 14,4	(12)	25,9 ± 11,9	(31)	0,497
Hypersalivation	19,54 ± 9,5	(21)	30,5 ± 12,3	(22)	0,003
Constipation	22,5 ± 10,9	(7)	25,6 ± 12,9	(36)	0,560
Sleep disorders	18,6	(1)	25,3 ± 12,6	(42)	0,604
Pain	18,2 ± 10,6	(5)	26,7 ± 12,5	(35)	0,082
Weight loss	24,4 ± 11,8	(10)	25,3 ± 12,9	(33)	0,846
Anosmia	25,4 ± 14,8	(24)	24,8 ± 9,3	(19)	0,884
Fatigue	18,5 ± 8,3	(19)	30,3 ± 13	(24)	0,018
Excessive sweating	25,3 ± 12,1	(35)	24,2 ± 15,2	(8)	0,750

OCD Obsessive Compulsive Disorder

Multiple linear regression analysis revealed a significant positive correlation between the MDS-NMS score (β = 0.448; P = 0.001), the H&Y scale (β = 0.399; P = 0.002), and the PDQ-39 score. The correlation between the NMSS and PDQ-39 scores is shown in Fig. 1.

Fig. 1.

Linear regression curve of the correlation between the PDQ-39 and the MDS-NMS scores

Similarly, a multiple linear regression analysis between each of the non-motor symptoms and the PDQ-39 score revealed a positive correlation between depression (β = 0,398; P = 0,043), cognitive deficits (β = 0,355; P = 0,046) and PDQ-39 scores. The correlation between the various non-motor signs and the PDQ-39 score is summarized in Table 6.

Table 6.

Multiple linear regression model of the PDQ-39 score

	R 2	Coef stand Beta	t	P-value
Depression	0,757	0,398	2,119	0,043
Anxiety		-0,082	-0,580	0,567
Apathy		0,229	1,229	0,230
Psychosis		-0,094	-0,717	0,480
Orthostatic hypotension		0,080	0,670	0,508
Urinary disorders		0,115	0,932	0,359
Sexual dysfunction		-0,148	-1,154	0,259
Pain		0,146	0,895	0,378
Weight loss		-0,010	-0,074	0,942
Anosmia		0,003	0,019	0,958
Fatigue		0,221	1,492	0,147
Cognitive disorders		0,355	2,088	0,046
Sleep disorders		-0,142	-1,032	0,311
OCD		0,205	1,421	0,167
Gastrointestinal disorders		0,103	0,766	0,451

OCD Obsessive compulsive disorders

Discussion

Our study determined the prevalence of non-motor signs in patients with IPD, assessed their quality of life, and the impact of these non-motor signs on their quality of life. To the best of our knowledge, this is the first study in the literature from sub-Saharan Africa to have assessed the non-motor signs of IPD using the MDS-NMS score and the first to have assessed the impact of these non-motor signs on the quality of life of Parkinsonian patients. Our findings are consistent with previous large-scale studies., which established that non-motor symptoms are key drivers of impaired quality of life in Parkinson’s disease across diverse populations [8].

There was a male predominance in our cohort, with a sex ratio of 5 men to 1 woman. This result is in line with most of the data in the literature, which report a clear male predominance in the epidemiology of IPD [6, 12, 18–20]. The mean age of the patients was 64.6 ± 10 years, with a mean age of onset of 59.6 ± 11.7 years. Similar data have been reported by several studies, with the mean age at disease onset ranging from 57 to 62 years [7, 21]. Nearly 10% of patients had young-onset Parkinson’s disease (age of onset less than 50 years). Although the sample size was insufficient to allow for a robust subgroup analysis, early-onset Parkinson’s disease has been associated with a distinct non-motor symptom profile, particularly with respect to neuropsychiatric and sleep-related symptoms. This has been highlighted in recent studies, and future research in larger African cohorts should further explore these differences [22].

The overall prevalence of non-motor symptoms in our study was high (100%), with all patients presenting with at least one non-motor sign, consistent with findings reported by some authors worldwide [18, 20, 22], who found NMS prevalence ranging from 98.6 to 100%. Similar to the findings of studies conducted in Portugal and China [21, 23], the most prevalent non-motor symptom was constipation, followed by pain, sleep disorders (dominated by REM behavioral disorder), urinary disorders (dominated by urinary urgency), and apathy. Other authors reported a predominance of fatigue, anxiety-depressive disorders, urinary disorders, and cognitive disorders [18–20, 24].

More than half of our patients presented with a sexual disorder, mostly consisting of erectile dysfunction. Our results corroborate the findings of Signer and Shalash, who respectively reported a prevalence of 60% and 43.5% of erectile dysfunction in Parkinson’s patients [19, 25].

The frequencies of fluctuation by category of non-motor symptoms were low, and the non-motor symptoms for which the most fluctuations were observed were urinary disorders and excessive sweating. Our results differ from those of Rodriguez-Blazquez, who reported a high fluctuation in fatigue and anxiety, with a prevalence of 68.5% and 62.4%, respectively [26]. This difference in findings may be due to the differences in the study population and disease stage, as they included mostly caucasian patients with a median disease stage of 2, while our study had a black African patient population with a median disease stage of 2.5. Non-motor fluctuations remain an under-recognized but clinically important aspect of PD. Their identification is essential, as they may significantly impact patient well-being and may respond to treatment adjustments. However, detailed correlations with dopaminergic therapy could not be explored in our study due to limited medication data, representing an area for future research. Moreover, the genetic and environmental factors that contribute to Parkinson’s disease may also influence the specific non-motor symptoms experienced by patients, and these factors may differ between populations. There is increasing recognition that Parkinson’s disease may present differently across ethnic and geographic populations. Studies such as this have highlighted disparities in recruitment into clinical trials, while the EmPark study has demonstrated variations in clinical phenotypes and societal impact across Middle Eastern populations. These findings underscore the importance of acquiring data from underrepresented regions, particularly Sub-Saharan Africa, where both clinical profiles and health system factors may differ substantially. Our study contributes to addressing this gap.

Despite the high prevalence of non-motor symptoms, our patients’ quality of life was not significantly affected (mean PDQ-39 index 23.45 ± 12.5%). The dimensions of quality of life most affected were, in order of frequency: cognitive impairment, daily activities, mobility, and physical discomfort. Our results are consistent with those of Tibar, Martinez-martin, and Prakash, who all reported high scores for the cognitive domain, mobility, and activities of daily living [8, 27, 28]. These results could be explained by the fact that, in IPD, motor disability is at the forefront of the clinical picture, progressively hampering patients’ mobility and activities of daily living. Unlike motor symptoms, non-motor symptoms are often underrecognized and undertreated, and many patients adapt to them over time. Some non-motor symptoms, such as depression, anxiety, and psychosis, may have a greater impact on quality of life compared to other non-motor symptoms such as anosmia or constipation. Several authors have demonstrated the negative impact of cognitive disorders on the quality of life of Parkinson’s patients [29, 30]. The dimension least affected was social support. The majority of our patients were well cared for by family and friends and had sufficient support. Patients suffered little stigma or psychological discomfort (stigma score 12.5%) and seemed to have accepted their condition.

There was a positive correlation between the MDS-NMS non-motor signs score and the PDQ-39 quality-of-life score in our study. The MDS-NMS score was an independent predictor of poor quality of life in our study. This suggests that quality of life declines as patients develop more non-motor signs. Our results are consistent with several prior studies conducted among patients of different races [7, 8, 18, 19, 23]. Similarly, there was a positive correlation between the quality-of-life score and the severity of motor disability assessed by the Hoehn and Yahr scale, suggesting that the quality of life of patients with Parkinson’s disease diminishes progressively as their motor disability worsens. This result corroborates most of the studies in the literature [7, 28].

Multiple linear regression analysis, including the quality of life score as the dependent variable and each of the non-motor signs as explanatory variables, showed that neuropsychiatric symptoms, particularly depression and cognitive disorders, were independent predictors of poor quality of life in Parkinson’s patients in our study. Mood disorders and cognitive impairment have long been recognized as key non-motor symptoms affecting quality of life in Parkinson’s disease, as they can have a profound impact on functional status, independence, and social engagement. Shalash in 2018 found depression to be the main factor predicting poor quality of life (R² = 0.497, p < 0.0001) [19]; Gallagher in his 2010 series reported that depression was the independent predictive factor most strongly associated with poor quality of life (R² = 0.53, p = 0.005) [31]. Additionally, Antonini demonstrated that treating depression in depressed Parkinson’s patients significantly improved their quality of life [32]. Depression can lead to decreased motivation, apathy, and social withdrawal, while cognitive deficits can interfere with the ability to carry out activities of daily living. Additionally, neuropsychiatric symptoms such as hallucinations and delusions can be particularly distressing and disabling for patients and carers. Moreover, cognitively impaired patients may be less able to adhere to medications, report and describe their symptoms, making recognition and treatment of non-motor issues more challenging. Appropriate management of non-motor symptoms, particularly neuropsychiatric issues, is crucial for improving quality of life in Parkinson’s disease.

Comparing quality of life between patients with and without non-motor symptoms for each category of non-motor signs, it was found that depression, fatigue, urinary urgency, and hypersalivation were significantly associated with poorer quality of life in patients with these different symptoms than in those without. These results are similar to those of Martinez-Martin in 2013, who reported, for each category of non-motor symptoms, a significant association between having the non-motor symptom and impairment in quality of life [8]. Similarly, Karri in 2020 reported a significant association between having mood disorders, cognitive disorders, gastrointestinal disorders, and sleep disorders and impaired quality of life [18]. This highlights the importance of incorporating comprehensive screening and management of non-motor symptoms into routine care for patients with Parkinson’s.

Conclusion

In our cohort, non-motor symptoms were significantly associated with poorer quality of life, while motor severity assessed by the Hoehn and Yahr stage also contributed substantially. These findings underscore the need for increased awareness, screening, and treatment of non-motor symptoms as an integral component of holistic Parkinson’s care.

Strengths and limitations

To the best of our knowledge, this study is among the first in Sub-Saharan Africa and the first in Senegal to comprehensively assess non-motor symptoms using the MDS-NMS and to examine their impact on quality of life. The use of validated and internationally recognized instruments enhances the reliability and comparability of our findings. Additionally, our study provides data from an underrepresented population, contributing valuable insights into the clinical profile of Parkinson’s disease in a resource-limited setting. However, a detailed motor symptom assessment was not performed. Although disease severity was assessed using the Hoehn and Yahr staging and showed a significant association with quality of life, the absence of detailed motor data limits a comprehensive evaluation of the relative contributions of motor and non-motor symptoms. A detailed motor symptom evaluation using standardized instruments, such as the MDS-UPDRS, was not performed. Consequently, the confounding effect of motor symptoms on quality of life could not be fully controlled, and motor disability may have acted as a confounding factor in the observed associations between non-motor symptoms and quality of life. Second, the cross-sectional design precludes causal inference regarding the relationship between non-motor symptoms and quality of life. Third, the relatively small sample size and single-center recruitment may limit the generalizability of the findings. Finally, some non-motor symptoms were assessed through patient self-report, which may introduce recall or reporting bias. Future studies with larger multicenter cohorts and detailed motor assessments are needed to better characterize the interplay between motor and non-motor symptoms in PD in Sub-Saharan Africa.

Authors’ contributions

AM contributed to protocol development, data collection, data analysis, and manuscript drafting. MN translated the manuscript into English. MB, EM, CK, and KN participated in data collection. FK, MF, AD, JK, MN, AG, and MN contributed to manuscript revision.

Funding

The authors declare that no funding was received for the conduct of this study or the preparation of this manuscript.

Data availability

The datasets generated and analyzed during the current study, including statistical outputs, are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was approved by the institutional ethics committee of Cheikh Anta Diop University in Dakar. We obtained verbal and written informed consent from the patients after clearly informing them of the study’s objectives and activities. Confidentiality of data was ensured throughout the study. We confirm that we have read the Journal’s position on ethical publication and affirm that this work is consistent with those guidelines. All study procedures were performed in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.