Stroke signs knowledge and factors delaying acute stroke patients’ hospital arrival in the absence of stroke care systems. A Kinshasa multicenter cross-sectional study

doi:10.21203/rs.3.rs-3083631/v1

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Stroke signs knowledge and factors delaying acute stroke patients’ hospital arrival in the absence of stroke care systems. A Kinshasa multicenter cross-sectional study

https://doi.org/10.21203/rs.3.rs-3083631/v1

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Background

The global burden of stroke is overwhelming. Rapid recognition and early medical intervention are essential to reduce stroke-related mortality and long-term disability. This study aimed to evaluate awareness of stroke symptoms/signs and determine factors delaying the hospital arrival of patients with acute stroke in Kinshasa.

Methods

This multicentric cross-sectional study was conducted in all medical facilities in the city of Kinshasa, where medical imaging facilities for stroke diagnosis were available and operational 24 h a day. Patients with stroke and/or accompanying family members were interviewed using a standard questionnaire, and their medical records were reviewed. Factors independently associated with a late arrival (≥ 4.5 h) to the hospital were identified using the logistic regression test in forward multivariate analysis.

Results

Overall, 202 patients aged 57.9 ± 13.1 years (of whom 106 (52.5%) were men and 96 (47.5%) were women) were included in this study. Only 13% of the patients immediately associated the first symptoms with a stroke episode, and only 29% had the initiative to consult a hospital immediately. Only 10% consulted the recruitment hospital within 4.5 h. Factors independently associated with delayed arrival were age < 60 years (p = 0.014, adjusted odds ratio [aOR] 3.2), being unmarried (p < 0.043, aOR 2.1), low educational level (p = 0.026, aOR 3.4), attending revival churches (p = 0.037, aOR 2.1), lack of stroke awareness (p = 0.002, aOR 2.4), awake consciousness (p = 0.014, aOR 6.5), history of hypertension (p = 0.016, aOR 3.8), history of diabetes mellitus (p = 0.047, aOR 4.3), excessive alcohol consumption (p = 0.038, aOR 2.9), ischemic stroke (p = 0.045, aOR 5.0), and low NIHSS score (p = 0.037, aOR 3.3 ).

Conclusions

This study depicted a low stroke awareness rate and a much longer prehospital delay than evidence-based guidelines recommend and identified 12 factors that public health actions could target to promote the earliest management of stroke.

stroke

prehospital delay

signs

symptoms

Kinshasa

Stroke is a leading cause of death, disability, and dementia worldwide (1)]. According to the Global Burden of Disease Study 2019 (2)], the incidence and prevalence of stroke were 12.2 million and 101 million, respectively. The study also reported that stroke caused 143 million disability-adjusted life-years (DALYs) and 6.55 million deaths. Globally, stroke was also the third-leading level 3 cause of death and disability combined in 2019, after neonatal disorders and ischemic heart disease. Low-income countries pay the highest cost of stroke, with an age-standardized stroke rate 3.7 times higher and a stroke mortality rate 3.6 times higher than those in high-income countries. That year, 89.0% of stroke-related DALYs and 86.0% of all stroke-related deaths were recorded in lower-income, lower–middle-income, and upper–middle-income countries. Sub-Saharan Africa is currently experiencing an unprecedentedly increased stroke burden, with the highest age-standardized incidence, prevalence, and mortality rates for stroke (3)]. This region lacks a structured organization of stroke management.

Early recognition, early transportation by emergency medical services to dedicated stroke units, early brain imaging, and early medical intervention (4–7)] are the basis of stroke management. All guidelines for stroke management published in Africa (8)], Europe (9)], and the USA (5)] reiterate that an improvement in the prognosis of stroke is possible, not only in terms of reducing mortality but also in reducing disability and the risk of institutionalization, provided that care is organized in a specialized and structured chain from the place of stroke occurrence to home return.

Implementing these recommendations and educating the general public and healthcare personnel has proven effective (10, 11)]. The Democratic Republic of Congo (DRC) is among the many poor developing countries in Africa where all the above is almost non-existent. Previous studies on stroke awareness and factors affecting delayed hospital arrival of patients with stroke have been carried out mainly in Western countries, in an organized socioeconomic, cultural, and health system background that differs fundamentally from that of the DRC.

Thus, this study aimed to establish patients’ knowledge of stroke signs and determine factors delaying the hospital arrival of patients with acute stroke in Kinshasa, the capital of the DRC, as these data are still lacking. Results from this study can inform public health strategies for increasing the public’s recognition and response to stroke symptoms/signs and thus reduce stroke-related mortality and disability.

Study design and setting

This prospective multicenter cohort study included 202 patients with confirmed stroke diagnosis between May and October 2022 in 12 hospitals in the city of Kinshasa, the only one where brain imaging (computed tomography [CT] and/or magnetic resonance imaging [MRI]) was operational and available 24 h a day during the study period.

Patient selection

Patients of any sex and age admitted to one of the recruiting hospitals, with a neuroradiologically confirmed stroke, were considered eligible for this study. Patients whose timing of stroke onset was unknown, whose stroke occurred while being admitted to the hospital (in-hospital stroke), and who refused to consent to participate in this study were excluded from this analysis.

Study procedures

Two trained finalist medical students, supervised by a cardiologist and a neurologist, both of the University of Kinshasa, interviewed in person all patients and/or their accompanying family members using a standardized questionnaire at the prehospital phase, defined as the time between stroke onset and admission to the recruitment hospital. The interviews were conducted at the bedside on the workday following admission. The questionnaire included sociodemographic questions (i.e., age, sex, marital status, level of education, religion, and occupation), known cardiovascular and neurological history (i.e., ischemic heart disease, obliterating arterial disease of the lower limbs, carotid angioplasty or coronary artery bypass surgery, heart rhythm disorder, heart failure, chronic renal failure, neoplasia, pulmonary embolism, and previous stroke), risk factors (i.e., hypertension, sudden death, dyslipidemia, hyperuricemia, tobacco, and heavy alcohol intake), time at which the first stroke signs/symptoms were noted, nature of the first signs/symptoms, first person who noticed these signs/symptoms, recognition of these signs/symptoms as suggesting a stroke, decision to consult when the first signs/symptoms are recognized, mode of transport used to get to the recruiting hospital, and time between the onset of the first signs/symptoms and the arrival at the recruiting hospital. Each patient’s medical record was reviewed for vital signs, stroke severity, and brain imaging results. These data were recorded on an ad hoc data collection sheet.

Operational definitions

Knowledge of stroke signs was defined as the patient’s awareness that the presenting signs/symptoms could be due to a stroke. Educational level was dichotomized into high (academic) or low (below academic level). The first time the patient or a witness noted clear neurological signs was defined as the stroke onset time. Two stroke categories were categorized: day-onset stroke, when first stroke signs occurred between 6:00 and 18:00, and night-onset stroke when first signs of stroke occurred between 18:00 and 6:00. Prehospital delay was calculated based on the stroke onset time and time of arrival at the recruitment hospital.

Patients who arrived at the recruitment hospital within 4.5 h of stroke onset were classified as “early arrivals”; all other patients were classified as “late arrivals.” Stroke severity was categorized according to the National Institutes of Health Stroke Scale (NIHSS) score and classified into five levels: NIHSS ≤ 5 (minor), NIHSS 6–10 (mild), NIHSS 11–15 (moderate), NIHSS 16–20 (severe), and NIHSS > 20 (very severe) (12)]. A high NIHSS score was defined as NIHSS ≥ 16. The stroke subtype (ischemic or hemorrhagic) was determined by brain imaging (CT or MRI).

Statistical analyses

Qualitative data were represented in absolute and relative frequency (%), and quantitative data in the form of the standard deviation of the mean (if normal distribution). Pearson’s Chi-square or Fischer’s exact test was used to compare proportions. Student’s t-test was used to compare the means of two groups with normal distributions. The normal distribution of each variable was assessed by the Kolmogorov–Smirnov test. The search for determinants of late arrival was performed using the logistic regression test in the forward multivariate analysis. The calculated adjusted odds ratio estimated the degree of association between the consultation delay and independent variables.

A value of p < 0.05 was considered the threshold of statistical significance. IBM SPSS Statistics for Windows version 24 (IBM Corp., Armonk, NY) was used to analyze all data.

Ethical considerations

The Comité National d’Etique de la Santé (National Health Ethics Committee) reviewed and approved this study, and all the included patients signed written informed consent. The rules of confidentiality and ethics have been respected according to the 1964 Declaration of Helsinki.

General characteristics of the study population

The study population consisted of 202 patients, of whom 106 were men (52.5%) and 96 were women (47.5%), with a sex ratio of 1.1 (in favor of men).

Sociodemographic characteristics of the study population

Table 1 shows the sociodemographic characteristics of the study population. The mean age of the patients was 57.9 ± 13.1 years (extremes 21 and 96 years), and most patients (32.2%) were between 51 and 60 years old. The majority were married and had a low academic level. Catholic religion and revival churches were the most popular religious denominations of the study population. Unemployed and public servants were the most represented occupational groups.

Table 1

Sociodemographic characteristics of the study population
Variables	Whole group (%) n = 202
Age (years)	57.9 ± 13.1
< 40	15 (7.4)
40–50	43 (21.3)
51–60	65 (32.2)
61–70	36 (17.8)
> 70	43 (21.3)
Sex
Male	106 (52.5)
Female	96 (47.5)
Civil status
Married	118 (58.4)
Unmarried	26 (12.9)
Divorced	15 (7.4)
Widower	43 (21.3)
Education level
Low	128 (63.4)
Academic	74 (36.6)
Religion
Catholic	74 (36.6)
Protestant	26 (12.9)
Kimbanguist	13 (6.4)
Revival church	73 (36.1)
Others	16 (7.9)
Occupation
Unemployed	60 (29.7)
State official	57 (28.2)
Artisan/merchant	45 (22.3)
Corporate agent	40 (19.8)

[Table 1]

Symptomatologic characteristics and patient alertness

Table 2 illustrates the symptomatology of the study population by sex. Most patients reported motor deficits (hemiparesis and hemiplegia) and impaired consciousness. In more than half of the cases, the patient noticed the first symptoms. In addition, only 13% of the patients immediately associated the first symptoms with a stroke episode, and only 29% had the initiative to consult a hospital immediately. Ignorance of the state of emergency and the hope of spontaneous remission have been put forward as the reason for the delay in taking the initiative to consult by 45% and 19% of the patients, respectively.

Table 2

Symptomatologic characteristics and patient alertness
Variables	Frequency	Percentage
First sign/symptom noted by the patient or the witness
Facial paralysis	35	17.3%
Impaired consciousness	60	29.7%
Impaired sensitivity	19	9.4%
Slurred speech	36	17.8%
Blurred vision	6	3.0%
Balance disorder	8	4.0%
Hemiparesis or hemiplegia	95	47.0%
Headache	43	21.3%
Dizziness	29	14.4%
Sphincter release	4	2.0%
Others	59	29.2%
Who first noticed these signs/symptoms
Patient himself	115	56.9%
Spouse/family member	76	37.6%
Colleague	7	3.5%
A stranger	4	2.0%
Stroke awareness
No	175	86.6%
Yes	27	13.4%
Decision to immediately contact the EMS
No	143	70.8%
Yes	59	29.2%
Reasons for the delayed decision to contact EMS
Ignorance of the state of emergency	91	45.0%
Hope for spontaneous remission	38	18.8%
Financial problem	17	8.4%
Security issue	11	5.4%
Transport problem	11	5.4%
Logistical problem	8	4.0%
Other reason	4	2.0%
EMS, emergency medical services

[Table 2]

Cardiovascular risk factors and history of stroke in the study population

As illustrated in Table 3, hypertension, heavy alcohol intake, and cigarette smoking were the most common cardiovascular risk factors in the study population. Approximately 82% of the patients were in their first episode of stroke, whereas 16% were in their second episode, and only 2% were in their third or more episodes.

Table 3

Patient history and stroke type by sex
Variables	Frequency	Percentage
Age > 55 years	113	55.9%
Hypertension	124	61.4%
Diabetes	35	17.3%
Dyslipidemia	1	0.5%
Hyperuricemia	2	1.0%
Cigarette smoking	34	16.8%
Heavy alcohol intake	70	34.7%
Stroke episode recurrence
First episode	166	82.2%
Second episode	32	15.8%
Third episode or more	4	2.0%
Existence of sequelae from previous episodes?
No	12	33.3%
Yes	24	66.7%
Type of the current stroke
Ischemic	12	33.3%
Hemorrhagic	7	19.4%
Undetermined	17	47.2%

[Table 3]

Timestamps and patient trajectories

Table 4 describes the time frame, timing of key events, and patient trajectory from the stroke site to the recruiting hospital. Day-onset stroke was predominant. Moreover, nearly three-quarters of the population had gone through other medical centers before arriving at the recruitment center. Only 10% consulted the recruiting hospital within 4.5 h; in most patients, the time from stroke onset to presentation was ≥ 24 h. The most common means of transport used to reach the hospital were public transport, a neighbor’s car, and a personal car at 66%, 16%, and 11.4%, respectively. Only 4% of the patient population utilized medical transport (ambulance). A small proportion (2.5%) of the patients used a motorcycle taxi to arrive at the hospital.

Table 4

Timestamps and patient trajectories
Variables	Frequency	Percentage
Stroke onset time
Day-onset	131	64.9%
Night-onset	71	35.1%
First hospital contact
Peripheral center/hospital	149	73.8%
Recruitment hospital emergencies	53	26.2%
Time from stroke onset to presentation at the recruiting hospital
≤ 4 h	22	10.9%
6–12 h	62	30.7%
12–24 h	38	18.8%
≥ 24 h	80	39.6%
Conveyance
Personal car	23	11.4%
Neighbor/brother’s car	32	15.8%
Transit car/bus	134	66.3%
Motorcycle taxi	5	2.5%
Ambulance	8	4.0%

[Table 4]

Stroke severity and brain imaging

As shown in Table 5, most (54.9%) patients had a moderate stroke; 9.4%, minor; 19.8%, mild; 10.4%, severe stroke; and 5.4%, very severe stroke. In nearly all patients, the stroke diagnosis was obtained with a brain scanner, and MRI was used for only one patient. Ischemic stroke was predominant in the study population.

Table 5

Stroke severity and brain imaging
Variable	Frequency	Percentage
NIHSS score
≤5 (minor)	19	(9.4)
6–10 (mild)	40	(19.8)
11–15 (moderate)	111	(54.9)
16–20 (severe)	21	(10.4)
>20 (very severe)	11	(5.4)
Brain imaging for diagnosis
MRI	1	(0.5)
CT	201	(99.5)
Stroke type
Ischemic	142	(70.3)
Hemorrhagic	60	(29.7)
CT, computed tomography; MRI, magnetic resonance imaging; NIHSS, National Institutes of Health Stroke Scale

[Table 5]

Determinants of late arrival in the recruiting hospital

As illustrated in Table 6, the analysis adjusted for factors determining the late arrival of patients with stroke showed that age < 60 years, being unmarried, low educational level, attending revival churches, lack of stroke awareness, awake consciousness, hypertension, diabetes, excessive alcohol consumption, ischemic stroke, and low NIHSS score emerged as independent determinants of late arrival at the recruiting hospital.

Table 6

Factors associated with late hospital arrival
Variables	ß	E.S.	Wald	P	aOR (IC95%)
Age < 60 years (none vs. yes)	1.739	0.973	3.196	0.014	3.2 (1.1–4.2)
Sex (male vs. female)	1.600	1.155	1.918	0.166	1.9 (0.5–4.8)
Civil status (married vs. unmarried)	2.399	1.186	4.092	0.043	2.1 (1.1–3.9)
Education level (high vs. low)	1.215	1.088	1.247	0.026	3.4 (1.4–4.6)
Religion (all other religions vs. revival churches)	0.716	1.344	4.328	0.037	2.1 (1.4–4.0)
Occupation (employed vs. unemployed)	−2.975	1.560	3.639	0.056	0.5 (0.2–1.8)
Stroke onset time (day vs. night-onset)	−4.840	2.438	3.942	0.067	1.9 (1.2–2.9)
Facial paralysis (no vs. yes)	−2.131	1.578	1.822	0.177	0.9 (0.5–2.6)
Stroke awareness (no vs. yes)	−0.882	1.255	0.494	0.002	2.4 (1.2–2.8)
Dysarthria/aphasia (no vs. yes)	0.436	1.139	0.147	0.702	1.5 (0.2–4.4)
Awake consciousness (no vs. yes)	1.867	1.284	2.116	0.014	6.5 (3.5–8.0)
Headache (no vs. yes)	−0.574	1.280	0.202	0.653	0.6 (0.5–6.9)
Dizziness (no vs. yes)	0.912	1.802	0.256	0.613	2.5 (0.7–5.1)
Sphincter release (no vs. yes)	2.901	4.594	0.399	0.528	1.2 (0.2–1.9)
First signs noted by (other person vs. patient himself)	−2.088	1.557	1.798	0.180	0.4 (0.1–2.6)
Hypertension (no vs. yes)	0.597	2.010	0.088	0.016	3.8 (1.4–9.3)
Diabetes (no vs. yes)	2.657	1.338	3.943	0.047	4.3 (1.1–6.4)
Cigarette smoking (No vs. yes)	−1.243	1.797	0.478	0.489	0.3 (0.1–9.7)
Excessive alcohol intake (no vs. yes)	3.292	1.588	4.299	0.038	2.9 (1.2–4.4)
Stroke episode (≥ 2nd vs. 1st)	−0.231	0.864	0.071	0.789	0.8 (0.1–4.3)
SBP* (mmHg)	−0.022	0.023	0.923	0.337	1.0 (0.9–1.2)
DBP* (mmHg)	0.002	0.038	0.003	0.954	1.0 (0.9–1.1)
Heart rate* (bpm)	-0.010	0.020	0.232	0.630	1.0 (0.9–1.3)
SpO2* (%)	0.034	0.118	0.083	0.773	1.3 (0.8–1.6)
High vs. low NIHSS score	0.163	0.078	4.329	0.037	3.3 (0.2–4.9)
Hemorrhagic stroke type (none vs. yes)	−5.599	2.795	4.012	0.045	5.0 (2.6–8.9)
* Quantitative variables

[Table 6]

This study aimed to evaluate the stroke symptoms/signs awareness and identify factors that delay patients with acute stroke from arriving at the hospital. Previous studies with the same objectives were conducted under different socioeconomic and cultural conditions. In addition, different thresholds were used to define late hospital arrival: 3 h (13, 14)], 4 h (15)], and 4.5 h for most studies (16–18)], and even 6 h (19)] for others. The 4.5-h threshold used in this study is the standard time for administering intravenous thrombolysis.

This study showed a low rate (13%) of stroke awareness and identified 12 factors independently associated with late hospital arrival. This study found a male preponderance in stroke presentation, which is consistent with other African in-hospital-based studies (14, 20–25)], except for Kenyan (26)] and South African studies, where patients with stroke were predominantly female (27–29)]. These differences may reflect the combined effect of sex (i.e., biological characteristics, including their genetic, biological, and physiological expression), sex (in the sense of a social concept that includes gender identification, expression, roles, and stereotypes for women, men, and sex-diverse people) (30)], and gendered exposures (31)] in different regions.

The age of the patients was 57.9 ± 13.1 years, similar to those in most previous local studies (21, 22, 32, 33)] and studies in the Middle East and North Africa (34)], but higher than that in a recent Senegalese study (14)], with most of the participants in their 40s. It was lower than that in Kenyan studies where the stroke occurred, on average, in the 70s (26)]. Globally, although all age groups can be affected from birth, with an ascent that becomes exponential after the age of 65, evidence suggests that indigenous African populations experience stroke at a younger age (35, 36)]. An earlier age of onset is also evidenced in low–middle-income countries compared with high-income countries, regardless of sex, stroke subtype, and whether data were collected at the population or hospital level (37–39)]. This age variability of stroke occurrence could be explained by healthcare access and quality (40)] and could be attributed to the early onset of stroke risk factors such as hypertension in the DRC and elsewhere in Africa (41)].

Hypertension is the patients’ number one modifiable cardiovascular risk factor. The same has been found in the INTERSTROKE study for African countries included in the study (42)] and in other studies in DRC (21, 22, 43)] and elsewhere (14, 26)]. As the incidence of stroke increases in proportion to blood pressure (44)] and the decrease in stroke mortality over the past decade is attributed, at least in part, to aggressive treatment of hypertension (45)], hypertension is the leading modifiable contributor to stroke, with approximately 52% of all strokes attributable to hypertension (46)].

In this study, ischemic stroke was predominant (70%). This finding is consistent with the results of other studies (21, 26, 35, 36)] that revealed ischemic stroke as the predominant stroke phenotype. Genetic factors and geographical location potentially influence the occurrence of stroke type (35)].

In this study, 16% of the patients were in their second episode, whereas 2% were in their third or more episodes. This stroke recurrence rate is close to that reported in other African studies (47–49)] and eight countries from South, East, and Southeast Asia (50)], suggesting a need for strategies to improve secondary prevention.

Motor deficit and impaired consciousness were the most common symptoms reported by 47% and 29.7% of the patients, respectively. This finding of a motor deficit is the most frequently reported stroke warning sign, which is consistent with nearly all previous studies (17, 51–55)].

Patients with day-onset stroke were predominant (64.9%), similar to most previously available studies on circadian stroke onset (56–60)]. This rhythmicity of stroke onset through the nychthemeron, with a clearly diurnal predominance, has been observed for both hemorrhagic and ischemic strokes when taken broadly and by subtypes (58)].

Only 13% of patients immediately recognized their stroke warning signs. This is probably one of the lowest ever-reported rates of the awareness of stroke symptoms, as a recent systematic review of published articles from 2010 to 2020 revealed a knowledge rate of the signs/symptoms of stroke ranging from 23.6–87% (61)]. This great disparity in stroke awareness rates across studies probably reflects inequalities in access to stroke information, according to the general culture of the populations and the strategic approach of the health authorities in the fight against stroke.

Only 29% immediately searched for medical care, whereas international guidelines recommend requesting emergency services promptly at the first sign of stroke (5, 62)]. Only 10% consulted the recruiting hospital within 4.5 h; in most patients, the time from stroke onset to presentation was ≥ 24 h. To promote the earliest possible access to treatment, the evaluation of intervals between stroke onset and hospital arrival has been the subject of numerous studies (17, 27, 48, 63–65)] to determine the most appropriate interventions. They have produced divergent results, reflecting the methodological differences, cultural and educational differences of the populations studied, and differences in the health service organization in general and the stroke chain in particular.

This study identified 12 determinants of late arrival to the hospital: age > 60 years, being unmarried, low educational level, attending revival churches, lack of stroke awareness, awake consciousness, hypertension, diabetes, excessive alcohol consumption, ischemic stroke, and low NIHSS score.

The finding of age < 60 years as a determinant of late arrival is consistent with the study results of Masumoto et al. (66)], which showed that older participants appeared more interested in their health than younger participants.

Being unmarried appeared to double the likelihood of late hospital arrival. This finding is consistent with previous studies (67, 68)] showing that living alone is an important reason for late medical care searching because a married person has the added benefit of having a partner who can detect stroke signs early and help them reach the hospital in time.

Ashraf et al. (15)] found that higher educational status was associated with early arrival, and we found that low educational level multiplied the risk of late arrival by threefold, reflecting a lack of information.

Patients attending revival churches demonstrated a twofold risk of late arrival. These patients may rely first on prayer or subjective interventions by their pastor or providence, thus explaining this inertia (69)].

In this study, patients who could immediately associate their first symptoms with a stroke were twice as likely to arrive at the hospital on time. This shows the importance of popularizing the knowledge of stroke signs in the general population.

Having comorbidities such as hypertension or diabetes emerged as a determinant of late arrival. The same finding was reported previously (15)]. Therefore, a major training and awareness-raising effort is required for these at-risk persons.

Patients with excessive alcohol intake were more likely to arrive late. The analysis of the Emerging Risk Factors Collaboration and UK Biobank cohorts (70)] found that average alcohol use is correlated with an increased risk of fatal and non-fatal total stroke. If a drunken patient experiences a stroke, prompt use of emergency services is unlikely, depending on the assistance of the witnesses, if any.

Hemorrhagic stroke reduced the risk of late arrival by fivefold. This corresponds with the findings of Ashraf et al., who reported that hemorrhagic strokes are more associated with reduced delays than ischemic strokes (15)]. This suggests that the severity of the signs is an important stimulus to seek medical services urgently. This explanation would also justify the finding of previous studies that depicted significantly higher NIHSS in patients arriving early (15)], compared with the results of the present study that a high NIHSS score reduced the risk of late arrival.

Study strengths and limitations

This study is the first to provide background information on stroke awareness and factors delaying the hospital arrival of patients with acute stroke in Kinshasa. The multicentric nature of the study suggests excellent external validity; in other words, a good degree of applicability of the results. However, this study is limited by its analysis of a relatively small sample. Further studies over a longer period are needed to ensure the inclusion of a larger sample size and ascertain the findings.

This study depicted a low rate of stroke awareness and a much longer prehospital delay than evidence-based guidelines recommend. The study identified 12 factors that public health actions could target to promote the earliest management of stroke.

aOR: adjusted odds ratio

CT: computed tomography

DALYs: disability-adjusted life-years

DRC: Democratic Republic of Congo

MRI: magnetic resonance imaging

NIHSS: National Institutes of Health Stroke Scale

Ethics approval and consent to participate: This study was reviewed and approved by the “National Health Ethics Committee” (Reference n°450/CNES/BN/PMMF/2022), and all the included patients signed written informed consent. All methods have been performed according to the 1964 Declaration of Helsinki.

Consent for publication: Not applicable.

Availability of data and materials: Because the consent given by study participants did not include data sharing with third parties, anonymized data can be made available to investigators for analysis on reasonable request to the corresponding author.

Competing interests: The authors declare that they have no competing interests.

Funding: Not applicable.

Authors’ contributions:

Design and concept of study: IKK, CKN, and BKP. Acquisition of data: IKK and CKN. Manuscript draft: IKK, CKN, and BKP. Analysis and interpretation of data: ANN and BKP. All authors have read and approved the final manuscript.

Acknowledgments:

Not applicable

Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, et al. Heart disease and stroke statistics-2019 update: a report from the American Heart Association. Circulation. 2019;139:e56-528.
GBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet Neurol. 2021;20:795-820.
Owolabi M, Sarfo FS, Akinyemi R, Gebreyohanns M, Ovbiagele B. The Sub-Saharan Africa Conference on Stroke (SSACS): an idea whose time has come. J Neurol Sci. 2019;400:194-8.
Frank A. The latest national clinical guideline for stroke. Clin Med (Lond). 2017;17:478.
Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K, et al. Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2019;50:e344-418.
Fonarow GC, Zhao X, Smith EE, Saver JL, Reeves MJ, Bhatt DL, et al. Door-to-needle times for tissue plasminogen activator administration and clinical outcomes in acute ischemic stroke before and after a quality improvement initiative. JAMA. 2014;311:1632-40.
Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K, et al. 2018 Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2018;49:e46-110.
Cockburn L, Fanfon TN, Bramall A, Ngole EM, Kuwoh P, Anjonga E, et al. Best practice guidelines for stroke in Cameroon: an innovative and participatory knowledge translation project. Afr J Disabil. 2014;3:92.
Quinn TJ, Richard E, Teuschl Y, Gattringer T, Hafdi M, O'Brien JT, et al. European Stroke Organisation and European Academy of Neurology joint guidelines on post-stroke cognitive impairment. Eur J Neurol. 2021;28:3883-920.
El Khoury R, Jung R, Nanda A, Sila C, Abraham MG, Castonguay AC, Zaidat OO. Overview of key factors in improving access to acute stroke care. Neurology. 2012;79:S26-34.
Sozener CB, Barsan WG. Impact of regional pre-hospital emergency medical services in the treatment of patients with acute ischemic stroke. Ann N Y Acad Sci. 2012;1268:51-6.
Koton S, Patole S, Carlson JM, Haight T, Johansen M, Schneider ALC, et al. Methods for stroke severity assessment by chart review in the atherosclerosis risk in communities study. Sci Rep. 2022;12:12338.
Dymm BL, Kwicklis M, Meurer WJ, Shi X, Lisabeth LD. Recurrent stroke arrival time. J Stroke Cerebrovasc Dis. 2023;32:107069.
Damon MS, Basse AM, Sow AD, Bassole PR, Diop-Sene MS, Banzouzi FL, et al. Pre-hospital delay in patients with ischemic stroke in the Fann Teaching Hospital, Dakar, Senegal, in 2020. Pan Afr Med J. 2022;41:79.
Ashraf VV, Maneesh M, Praveenkumar R, Saifudheen K, Girija AS. Factors delaying hospital arrival of patients with acute stroke. Ann Indian Acad Neurol. 2015;18:162-6.
Fladt J, Meier N, Thilemann S, Polymeris A, Traenka C, Seiffge DJ, et al. Reasons for prehospital delay in acute ischemic stroke. J Am Heart Assoc. 2019;8:e013101.
Terecoasa EO, Radu RA, Negrila A, Enache I, Casaru B, Tiu C. Pre-hospital delay in acute ischemic stroke care: current findings and future perspectives in a tertiary stroke center from Romania-a cross-sectional study. Medicina (Kaunas). 2022;58.
Kharbach A, Obtel M, Achbani A, Aasfara J, Hassouni K, Lahlou L, Razine R. Ischemic stroke in Morocco: prehospital delay and associated factors. Rev Epidemiol Sante Publique. 2021;69:345-59.
Yao S, Liu X, Luo Y, Liu Y, Huang L, Zhao J. How strong the predictive ability of the variable age and stroke severity for patients presented late. J Stroke Cerebrovasc Dis. 2020;29:104538.
Tshilanda M, Kanmounye US, Kapongo R, Tshiasuma M. Systemic disorders and the prognosis of stroke in Congolese patients: a cross-sectional study. Ghana Med J. 2020;54:225-30.
Tshikwela ML, Londa FB, Tongo SY. Stroke subtypes and factors associated with ischemic stroke in Kinshasa, Central Africa. Afr Health Sci. 2015;15:68-73.
Limbole EB, Magne J, Lacroix P. Stroke characterization in Sub-Saharan Africa: Congolese population. Int J Cardiol. 2017;240:392-7.
Otubogun FM, Akinyemi R, Ogunniyi S. Burden of adult neurological diseases in Odeda Area, Southwest Nigeria. BMJ Neurology Open. 2020;2:e000062.
Adoukonou T, Yahouédéou B, Agbétou M, Hountada H, Choki B, Kossi O, et al. Prevalence of stroke survivors in Parakou in northern Benin: a door-to-door community survey. Rev Neurol (Paris). 2020;176:839-45.
Siddiqi OK, Atadzhanov M, Birbeck GL, Koralnik IJ. The spectrum of neurological disorders in a Zambian tertiary care hospital. J Neurol Sci. 2010;290:1-5.
Waweru P, Gatimu SM. Stroke epidemiology, care, and outcomes in Kenya: a scoping review. Front Neurol. 2021;12:785607.
Khalema D, Goldstein LN, Lucas S. A retrospective analysis of time delays in patients presenting with stroke to an academic emergency department. SA J Radiol. 2018;22:1319.
Daffue K, Joubert G, Otto S. Computed tomography stroke findings and population demographics at Pelonomi Hospital, Bloemfontein. SA J Radiol. 2016;20:1-6.
Connor MD, Modi G, Warlow CP. Differences in the nature of stroke in a multiethnic urban South African population: the Johannesburg hospital stroke register. Stroke. 2009;40:355-62.
Mauvais-Jarvis F, Merz NB, Barnes PJ, Brinton RD, Carrero J-J, DeMeo DL, et al. Sex and gender: modifiers of health, disease, and medicine. Lancet. 2020;396:565-82.
Norris CM, Johnson NL, Hardwicke-Brown E, McEwan M, Pelletier R, Pilote L. The contribution of gender to apparent sex differences in health status among patients with coronary artery disease. J Women's Health. 2017;26:50-7.
Mpembi Nkosi M, Miezi MMS, Massamba Kubuta V, Matonda Ma Nzuzi T, Dubois V, De Partz MP, et al. Clinical course of post-stroke depression in Kinshasa. Rev Neurol (Paris). 2014;170:614-20.
Longo-Mbenza B, Lelo Tshinkwela M, Mbuilu Pukuta J. Rates and predictors of stroke-associated case fatality in black Central African patients. Cardiovasc J Afr. 2008;19:72-6.
Al-Rukn S, Mazya M, Akhtar N, Hashim H, Mansouri B, Faouzi B, et al. Stroke in the Middle East and North Africa: a 2-year prospective observational study of intravenous thrombolysis treatment in the region. Results from the SITS-MENA registry. Int J Stroke. 2020;15:980-7.
Owolabi M, Sarfo F, Howard VJ, Irvin MR, Gebregziabher M, Akinyemi R, et al. Stroke in indigenous Africans, African Americans, and European Americans: interplay of racial and geographic factors. Stroke. 2017;48:1169-75.
Akinyemi RO, Ovbiagele B, Akpalu A, Jenkins C, Sagoe K, Owolabi L, et al. Stroke genomics in people of African ancestry: charting new paths. Cardiovasc J Afr. 2015;26:S39-49.
Gezmu T, Schneider D, Demissie K, Lin Y, Gizzi MS. Risk factors for acute stroke among South Asians compared to other racial/ethnic groups. PLOS ONE. 2014;9:e108901.
Feigin VL, Krishnamurthi RV, Parmar P, Norrving B, Mensah GA, Bennett DA, et al. Update on the global burden of ischemic and hemorrhagic stroke in 1990-2013: the GBD 2013 study. Neuroepidemiology. 2015;45:161-76.
Prince MJ, Wu F, Guo Y, Robledo LMG, O'Donnell M, Sullivan R, Yusuf S. The burden of disease in older people and implications for health policy and practice. Lancet. 2015;38:549-62.
Rahbar MH, Medrano M, Diaz-Garelli F, Gonzalez Villaman C, Saroukhani S, Kim S, et al. Younger age of stroke in low-middle income countries is related to healthcare access and quality. Ann Clin Transl Neurol. 2022;9:415-27.
Schutte AE, Botha S, Fourie CMT, Gafane-Matemane LF, Kruger R, Lammertyn L, et al. Recent advances in understanding hypertension development in sub-Saharan Africa. J Hum Hypertens. 2017;31:491-500.
O'Donnell MJ, Chin SL, Rangarajan S, Xavier D, Liu L, Zhang H, et al. Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (Interstroke): a case-control study. Lancet. 2016;388:761-75.
Musung JM, Kakoma PK, Bugeme M, Banze JPM, Mukeng CK, Muyumba ON, et al. Risk factors for hemorrhagic stroke among adults in the Democratic Republic of the Congo: a hospital-based study in a limited resource setting. Stroke Res Treat. 2022;2022:7840921.
KANNEL W. Epidemiologic of the role of blood pressure in stroke The Framingham study. JAMA. 1970;214:301-10.
Lackland DT, Carey RM, Conforto AB, Rosendorff C, Whelton PK, Gorelick PB. Implications of recent clinical trials and hypertension guidelines on stroke and future cerebrovascular research. Stroke. 2018;49:772-9.
O'Donnell MJ, Xavier D, Liu L, Zhang H, Chin SL, Rao-Melacini P, et al. Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the Interstroke study): a case-control study. Lancet. 2010;376:112-23.
Kaduka L, Korir A, Oduor CO, Kwasa J, Mbui J, Wabwire S, et al. Stroke distribution patterns and characteristics in Kenya's leading public health tertiary institutions: Kenyatta National Hospital and Moi Teaching and Referral Hospital. Cardiovasc J Afr. 2018;29:68-72.
Odour C, Keter A, Diero L, Siika A, Williams L. Stroke types, risk factors, quality of care and outcomes at a referral hospital in Western Kenya. East Afr Med J. 2015;92:324-32.
Shitandi OB. Pattern of stroke in a rural Kenyan hospital. Malawi Med J. 2019;3:50-5.
Chin YY, Sakinah H, Aryati A, Hassan BM. Prevalence, risk factors and secondary prevention of stroke recurrence in eight countries from South, East and Southeast Asia: a scoping review. Med J Malaysia. 2018;73:90-9.
Lecouturier J, Murtagh MJ, Thomson RG, Ford GA, White M, Eccles M, Rodgers H. Response to symptoms of stroke in the UK: a systematic review. BMC Health Serv Res. 2010;10:157.
Soto-Cámara R, González-Bernal JJ, González-Santos J, Aguilar-Parra JM, Trigueros R, López-Liria R. Knowledge on signs and risk factors in stroke patients. J Clin Med. 2020;9:2557.
Mellon L, Doyle F, Williams D, Brewer L, Hall P, Hickey A. Patient behaviour at the time of stroke onset: a cross-sectional survey of patient response to stroke symptoms. Emerg Med J. 2016;33:396-402.
Faiz KW, Sundseth A, Thommessen B, Rønning OM. Patient knowledge on stroke risk factors, symptoms and treatment options. Vasc Health Risk Manag. 2018:37-40.
Sundseth A, Faiz KW, Rønning OM, Thommessen B. Factors related to knowledge of stroke symptoms and risk factors in a Norwegian stroke population. J Stroke Cerebrovasc Dis. 2014;23:1849-55.
Ryu WS, Hong KS, Jeong SW, Park JE, Kim BJ, Kim JT, et al. Association of ischemic stroke onset time with presenting severity, acute progression, and long-term outcome: a cohort study. PLoS Med. 2022;19:e1003910.
Pariona-Vargas F, Mun KT, Lo EH, Starkman S, Sanossian N, Hosseini MB, et al. Circadian variation in stroke onset: differences between ischemic and hemorrhagic stroke and weekdays versus weekends. J Stroke Cerebrovasc Dis. 2023;32:107106.
Ripamonti L, Riva R, Maioli F, Zenesini C, Procaccianti G. Daily variation in the occurrence of different subtypes of stroke. Stroke Res Treat. 2017;2017:9091250.
Uddin M, Hoque M, Uddin M, Kamol S, Chowdhury R. Circadian rhythm of onset of stroke-in 50 cases of ischemic stroke. Mymensingh Med J. 2015;24:121-6.
Choi YI, Seo I, Kim D, Oh HG, Jeong DS, Park H, Yang K. Same pattern of circadian variation according to the season in the timing of ischemic stroke onset: preliminary report. Sleep Med Res. 2015;6:72-6.
Melak AD, Wondimsigegn D, Kifle ZD. Knowledge, prevention practice and associated factors of stroke among hypertensive and diabetic patients - a systematic review. Risk Manag Health Policy. 2021;14:3295-310.
Kobayashi A, Czlonkowska A, Ford GA, Fonseca AC, Luijckx GJ, Korv J, et al. European Academy of Neurology and European Stroke Organization consensus statement and practical guidance for pre-hospital management of stroke. Eur J Neurol. 2018;25:425-33.
Mark O'Meara R, Ganas U, Hendrikse C. Access to acute stroke care: a retrospective descriptive analysis of stroke patients' journey to a district hospital. Afr J Emerg Med. 2022;12:366-72.
Tong D, Reeves MJ, Hernandez AF, Zhao X, Olson DM, Fonarow GC, et al. Times from symptom onset to hospital arrival in the get with the guidelines–stroke program 2002 to 2009: temporal trends and implications. Stroke. 2012;43:1912-7.
Teuschl Y, Brainin M. Stroke education: discrepancies among factors influencing prehospital delay and stroke knowledge. Int J Stroke. 2010;5:187-208.
Masumoto K, Shiozaki M, Taishi N. The impact of age on goal-framing for health messages: the mediating effect of interest in health and emotion regulation. PLOS One. 2020;15:e0238989.
Alberts MJ, Bertels C, Dawson DV. An analysis of time of presentation after stroke. JAMA. 1990;263:65-8.
Jorgensen H, Nakayama H, Reith J, Raaschou H, Olsen T. Factors delaying hospital admission in acute stroke: the Copenhagen stroke study. Neurology. 1996;47:383-7.
Yu Y, Xiao L, Chamberlain DJ. Perceptions of care in patients from culturally and linguistically diverse background during acute and critical illness: an integrative literature review. Aust Crit Care. 2021;34:486-95.
Wood AM, Kaptoge S, Butterworth AS, Willeit P, Warnakula S, Bolton T, et al. Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies. Lancet. 2018;391:1513-23.

No competing interests reported.

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Stroke signs knowledge and factors delaying acute stroke patients’ hospital arrival in the absence of stroke care systems. A Kinshasa multicenter cross-sectional study

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Abstract

Background

Methods

Study design and setting

Patient selection

Study procedures

Operational definitions

Statistical analyses

Ethical considerations

Results

General characteristics of the study population

Sociodemographic characteristics of the study population

[Table 1]

Symptomatologic characteristics and patient alertness

[Table 2]

Cardiovascular risk factors and history of stroke in the study population

[Table 3]

Timestamps and patient trajectories

[Table 4]

Stroke severity and brain imaging

[Table 5]

Determinants of late arrival in the recruiting hospital

[Table 6]

Discussion

Study strengths and limitations

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

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