Correspondence to Dr Youli Jiang; [email protected]
STRENGTHS AND LIMITATIONS OF THIS STUDY
This study uses a robust retrospective design to analyse healthcare seeking delays among immigrant patients who had a stroke.
Comprehensive demographic and clinical data were collected and systematically analysed.
The lack of a non-migrant comparison group limits the ability to generalise findings beyond the immigrant population.
Introduction
Stroke is a leading global cause of morbidity and mortality, ranking among the top three causes of death worldwide1 and has emerged as the primary cause of death and disability in China.2 Acute ischaemic stroke (AIS), characterised by its high incidence and recurrence rates, plays a significant role in clinical mortality and morbidity, imposing substantial economic burdens on families and society. The most effective treatments—thrombolysis via intravenous administration of recombinant tissue plasminogen activator (rt-PA) or endovascular therapy—aim to promptly restore vascular reperfusion and improve cerebral tissue perfusion.3 Current international and national guidelines recommend the administration of rt-PA within 4.5 hours of AIS onset as the most effective intervention.4 Evidence-based studies underscore the time-dependent efficacy of thrombolysis, highlighting faster dissolution for better outcomes.5
AIS, as an acute cerebrovascular condition, necessitates timely medical intervention for favourable patient outcomes.6 According to the national guidelines for the prevention and treatment of cerebrovascular diseases, for this study, healthcare seeking delays are defined as the time from symptom onset to hospital arrival exceeding 2 hours, and are categorised into decision-making and transportation delays.7 Decision-making delay refers to the interval from symptom onset to the decision to seek medical attention, while transportation delay is the time taken to reach the hospital post-decision.8 According to a multicentre study report of 132 urban hospitals in China, only 78.5% of patients with AIS’ time from onset to arrival at the emergency room door exceeded the standard requirements.9 Furthermore, previous research has found that China’s immigrant population is a group with a high incidence of delayed medical treatment for patients with AIS.10 In immigrant populations, ischaemic stroke healthcare-seeking delays may be influenced by a multitude of factors, with the main reasons being decision-making delays (also known as patient delay, referring to the interval from symptom manifestation to the initial medical aid) and transportation delays (from initial aid to arrival at the emergency centre).11–14 Shenzhen, a burgeoning immigrant city with 60% of its population hailing from diverse national regions and social backgrounds, presents variances in disease recognition, potentially delaying timely medical support/services for immigrant populations.15 Previous research has identified immigrant populations as a significant group prone to healthcare seeking delays in stroke cases.15 However, there has been a lack of research in China focusing on healthcare-seeking delays among the stroke-affected immigrant demographic, with influencing factors varying across different regions and social backgrounds. This study aims to explore the status of healthcare-seeking delays and identify factors contributing to such delays in the immigrant stroke population, thereby providing evidence to support future policy formulation and research.
Method
Study participants
This retrospective observational study selected the immigrant population among patients who had a stroke diagnosed and treated in the Department of Neurology, Longhua District People’s Hospital, Shenzhen, from December 2021 to October 2023. The Department of Neurology of the hospital is one of the main units for stroke diagnosis and treatment in Shenzhen. In recent years, the number and level of stroke diagnoses and treatment have ranked among the top in the city. This study was selected from Shenzhen, a city with a characteristic immigrant population, and provides valuable insights for developing stroke diagnosis and treatment strategies in other major cities and regions with large immigrant populations. Inclusion criteria were as follows: (1) Patients aged 18 and above; (2) Diagnosis of AIS confirmed through diagnostic techniques including, but not limited to, CTA and MRI; (3) Individuals capable of providing verifiable identity data to ensure the authenticity of patient information; (4) immigrant population. Exclusion criteria included: (1) Patients with a final diagnosis other than stroke on discharge; (2) Incomplete data or lack of comprehensive information required for the study; (3) Duplicate ID numbers or patient entries.
Definition of concepts: immigrant population in Shenzhen is defined according to the latest Shenzhen Statistical Yearbook by the Shenzhen Bureau of Statistics as individuals holding non-Shenzhen city registered residence.16 Based on national guidelines for the prevention and treatment of cerebrovascular diseases, each critical stage from the onset of stroke symptoms to seeking help, from making a phone call to hospital arrival, and from admission to the initiation of treatment should not exceed 1 hour.7 Hence, healthcare-seeking delay in this study is defined as the time exceeding 2 hours from symptom onset to hospital arrival.
Survey instruments
Sociodemographic and clinical data
This section encompasses a comprehensive array of sociodemographic and clinical parameters essential for the study. It covers age, sex, ethnicity, living alone, stroke awareness, educational attainment, health insurance and mode of arrival at the hospital. Lifestyle habits such as smoking and drinking are also considered, alongside the solitary living situation. Clinical diagnostic data include the patients’ mode of hospital arrival, Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification and initial symptoms like limb weakness, consciousness disturbance, dizziness and aphasia. Additionally, ECG findings and medical history details, including prior stroke incidents, coronary heart disease, hyperlipidaemia and atrial fibrillation (AF), are integral to the analysis.
Modified Rankin Scale scoring
The Modified Rankin Scale (mRS) is used to assess the neurological function recovery of patients post-stroke, employing a 6-grade scoring system ranging from 0 to 5.17 Scores of 0–2 indicate a good prognosis, whereas scores ≥3 suggest a poor outcome. This scale aids physicians and patients in understanding poststroke recovery, facilitating the formulation of appropriate rehabilitation plans. Measurements are taken before and after the onset.
National Institutes of Health Stroke Scale scoring
The National Institutes of Health Stroke Scale (NIHSS) score evaluates the degree of neurological impairment in patients who had a stroke, covering aspects such as level of consciousness, gaze, visual fields, facial palsy, arm movement, leg movement, ataxia, sensory, language, dysarthria and neglect. The scale comprises 11 categories with 15 items, scored from 0 to 3 or 0 to 2, yielding a total score range of 0–42, where higher scores indicate more severe neurological deficits.
Glasgow Coma Scale scoring
The Glasgow Coma Scale (GCS) score is widely used in clinical settings like emergency departments and ICUs to assess patient consciousness levels. It encompasses three aspects: eye opening, verbal response and motor response, with a maximum score of 15 indicating full consciousness. Lower scores denote more severe consciousness impairments.
Data collection methods
Data collection is based on the Shenzhen Stroke Follow-up Platform. All data were obtained from the hospital’s electronic medical record system, which provides comprehensive and systematically recorded information. This includes sociodemographic data, healthcare-seeking information, in-hospital diagnosis and treatment details, and follow-up data. Symptom onset times were reported by patients or their family members during emergency department visits, based on careful recall, and were recorded by attending physicians. Arrival times and other relevant timestamps, such as emergency medical services (EMS) usage, were documented directly by the emergency or neurology department staff. The Shenzhen Stroke Follow-up Platform ensures data quality through dedicated full-time managers and data entry personnel who extract, input and manage data. To maintain data accuracy, all cases undergo regular quality checks, including periodic data audits and quality control meetings. The platform employs file encryption for data security and centralised management to ensure compliance with data integrity standards. This comprehensive approach to data collection enables robust analysis of healthcare-seeking delays among patients with AIS in the immigrant population, ensuring that findings are both reliable and applicable to broader public health strategies.
Statistical methods
Preliminary statistical descriptions were applied to all metric and percentage data. Continuous variables underwent normality testing, with t-tests used for normally distributed data between groups, or Mann-Whitney U tests otherwise. Categorical variables were analysed using χ2 tests, considering p values<0.05 statistically significant. Median healthcare-seeking delay times for each variable were recorded. Variables showing statistical differences in univariate analysis underwent stepwise forward logistic regression to identify independent factors associated with healthcare seeking delays, with significant factors adjusted and expressed as adjusted ORs (aOR) with 95% CIs. Violin plots visualised variables with significant delay rate differences, with further subgroup analysis on critical characteristic variables to identify primary groups experiencing healthcare seeking delays. All data analyses were steps performed using Python V.3.9.
Patient and public involvement
None.
Results
General information
In this retrospective observational study, we examined healthcare-seeking delays across a cohort of 1356 patients with AIS. Our analysis, as detailed in table 1, revealed that a substantial 82.6% (n=1120) of these patients experienced significant delays in seeking medical care. Male patients were slightly more prone to these delays (73.66%, n=825) compared with their female counterparts (26.34%, n=295). The association between educational attainment and the timing of hospital arrival was notable; those with junior high school education or lower were observed to experience more delays compared with individuals with a higher educational level. Stroke awareness also played a crucial role, with a 37.50% delay rate observed in those possessing some knowledge of stroke symptoms, in stark contrast to a 62.50% delay rate among those lacking this knowledge. Furthermore, the presence of health insurance and the mode of hospital arrival influenced delay durations, with insured patients and those referred through medical services (120, referral) generally achieving quicker admissions.
Table 1Baseline characteristics of the immigrant population among patients with acute ischaemic stroke
| Variable | Category | Delay, n=1120 (%) | Non-delay, n=236 (%) | P value | ODT median (hours) |
| Age | 57.82±13.15 | 56.80±14.06 | 0.24 | 12.67 | |
| Sex | Male | 825 (73.66) | 163 (69.07) | 0.17 | 12.65 |
| Female | 295 (26.34) | 73 (30.93) | 13.18 | ||
| Ethnicity | Han | 1084 (82.37) | 232 (17.63) | 0.30 | 12.5 |
| Others | 36 (90.00) | 4 (10.00) | 20.77 | ||
| Living alone | No | 1067 (82.66) | 224 (17.34) | 0.85 | 13.08 |
| Yes | 53 (81.94) | 12 (18.46) | 9.57 | ||
| Education level | Junior high school and below | 396 (84.62) | 72 (15.38) | 0.01 | 15.15 |
| High school | 505 (83.75) | 98 (16.25) | 11.97 | ||
| Bachelor's degree and above | 219 (76.84) | 66 (23.16) | 10.05 | ||
| Smoking | No | 345 (85.82) | 57 (14.18) | 0.05 | 13.16 |
| Yes | 775 (81.24) | 179 (18.76) | 11.98 | ||
| Drinking | No | 252 (84.56) | 46 (15.44) | 0.35 | 12.55 |
| Yes | 868 (82.04) | 190 (17.96) | 12.97 | ||
| Stroke awareness | Knowledgeable | 420 (69.65) | 183 (30.35) | <0.01 | 6.1 |
| Unaware | 700 (92.96) | 53 (7.04) | 19.35 | ||
| Health insurance | No | 279 (85.06) | 49 (14.94) | 0.20 | 13.39 |
| Yes | 841 (81.81) | 187 (18.19) | 12.37 | ||
| Wake-up stroke | No | 926 (80.59) | 223 (19.41) | <0.01 | 10.28 |
| Yes | 194 (93.72) | 13 (6.28) | 14.9 | ||
| Mode of arrival at the hospital | Transfer | 78 (82.98) | 16 (17.02) | <0.01 | 9.78 |
| Private | 947 (86.88) | 143 (13.12) | 16.8 | ||
| Ambulance | 795 (55.23) | 77 (44.77) | 2.25 | ||
| ECG findings | Normal | 1081 (83.92) | 203 (16.08) | <0.01 | 13.2 |
| Abnormal | 39 (54.17) | 33 (45.83) | 4.43 | ||
| Atrial fibrillation | No | 1081 (84.19) | 203 (15.81) | <0.01 | 23.6 |
| Yes | 39 (54.17) | 33 (45.83) | 3.99 | ||
| GCS | 3–8 | 1099 (83.45) | 218 (16.55) | <0.01 | 12.98 |
| 9–12 | 14 (46.67) | 16 (53.33) | 12.01 | ||
| 13–15 | 7 (77.78) | 3 (22.22) | 10.62 | ||
| Admission NIHSS | 0–4 | 972 (85.94) | 159 (14.06) | <0.01 | 15.67 |
| 5–20 | 112 (67.88) | 53 (32.12) | 6.72 | ||
| 21–42 | 36 (60.0) | 24 (40.0) | 4.89 | ||
| Premorbid mRS | 0–2 | 1052 (82.25) | 227 (17.75) | 0.23 | 12.75 |
| 3–5 | 68 (88.31) | 9 (11.69) | 12 | ||
| Admission mRS | 0–2 | 358 (86.27) | 57 (13.73) | 0.02 | 19.25 |
| 3–6 | 762 (80.98) | 179 (19.02) | 11.43 | ||
| Self-reported HBP | No | 794 (81.86) | 176 (18.14) | 0.29 | 12.67 |
| Yes | 326 (84.46) | 60 (15.54) | 12.63 | ||
| Self-reported DM | No | 1006 (81.66) | 226 (18.34) | 0.01 | 11.99 |
| Yes | 114 (91.94) | 10 (8.06) | 22.38 | ||
| Prehospital medication | No | 449 (81.79) | 100 (18.21) | 0.56 | 14.9 |
| Yes | 671 (83.15) | 136 (16.85) | 11.93 | ||
| TOAST classification | Large vessel stroke | 383 (82.01) | 84 (17.99) | <0.01 | 12.46 |
| Small vessel stroke | 356 (91.52) | 33 (8.48) | 16.13 | ||
| Cardioembolic stroke | 70 (61.95) | 43 (38.05) | 5.12 | ||
| Unexplained stroke | 35 (83.33) | 7 (16.67) | 18.29 | ||
| Other stroke | 271 (80.18) | 67 (19.82) | 12.94 | ||
| Recurrence of stroke | No | 907 (82.38) | 194 (17.62) | 0.73 | 12.68 |
| Yes | 213 (83.53) | 42 (16.47) | 12.28 | ||
| Myocardial infarction | No | 1108 (82.56) | 234 (17.44) | 0.98 | 12.59 |
| Yes | 12 (85.71) | 2 (14.29) | 18.19 | ||
| Dyslipidaemia | No | 1090 (82.45) | 232 (17.55) | 0.52 | 12.54 |
| Yes | 30 (88.24) | 4 (11.76) | 19.33 | ||
| CAD | No | 1082 (83.17) | 219 (16.83) | 0.01 | 12.87 |
| Yes | 38 (69.09) | 17 (30.91) | 11.1 | ||
| HBP | No | 296 (79.36) | 77 (20.64) | 0.06 | 12.5 |
| Yes | 824 (83.83) | 159 (16.17) | 12.78 | ||
| DM | No | 872 (81.57) | 197 (18.43) | 0.07 | 12.32 |
| Yes | 248 (86.41) | 39 (13.59) | 15.38 | ||
| Metabolic abnormalities | No | 945 (82.53) | 200 (17.47) | 0.96 | 12.4 |
| Yes | 175 (82.94) | 36 (17.06) | 14.27 | ||
| Weakness | No | 408 (36.43) | 44 (18.64) | <0.01 | 17.47 |
| Yes | 712 (63.57) | 192 (81.36) | 11.02 | ||
| Consciousness disorder | No | 1099 (98.12) | 219 (92.80) | <0.01 | 13.14 |
| Yes | 21 (1.88) | 17 (7.20) | 5.18 | ||
| Dizzy | No | 910 (81.25) | 211 (89.41) | <0.01 | 11.87 |
| Yes | 210 (18.75) | 25 (10.59) | 17.35 | ||
| Dysarthria | No | 733 (65.45) | 102 (43.22) | <0.01 | 19.25 |
| Yes | 387 (34.55) | 134 (56.78) | 8.33 | ||
| Vomit | No | 1107 (98.84) | 232 (98.31) | 0.73 | 12.87 |
| Yes | 13 (1.16) | 4 (1.69) | 6.52 | ||
| Deviated mouth | No | 1098 (98.04) | 233 (98.73) | 0.65 | 12.6 |
| Yes | 22 (1.96) | 3 (1.27) | 17.98 | ||
| Aphasia | No | 1118 (99.82) | 230 (97.46) | <0.01 | 12.83 |
| Yes | 2 (0.18) | 6 (2.54) | 1.34 | ||
| Other symptoms | No | 776 (69.29) | 184 (77.97) | 0.01 | 13.08 |
| Yes | 344 (30.71) | 52 (22.03) | 5.47 | ||
| Valvular heart disease | No | 1091 (97.41) | 222 (94.07) | 0.01 | 13.66 |
| Yes | 29 (2.59) | 14 (5.93) | 10.92 |
(n=1356).
CAD, Coronary Artery Disease; DM, diabetes mellitus; GCS, Glasgow Coma Scale; HBP, High Blood Pressure; mRS, Modified Rankin Scale; NIHSS, National Institutes of Health Stroke Scale; ODT, onset-to-door time; TOAST, Trial of Org 10172 in Acute Stroke Treatment.
Distribution of healthcare-seeking delay times
The distribution of healthcare-seeking delay times, illustrated in table 1, showcases a median delay of 12.67 hours among all patients with AIS. Notably, non-Han patients and those with lower educational achievements faced longer delays. Conversely, patients with AF and those presenting with abnormal ECG results at admission were associated with shorter delay times in seeking medical attention. Specifically, patients with self-reported diabetes mellitus (DM) encountered a median delay time of 22.38 hours, suggesting potential challenges in symptom recognition within this demographic (table 1).
Univariate analysis
In our univariate analysis, several factors showed significant differences in healthcare-seeking delays. Patients lacking stroke awareness experienced a higher delay rate (62.5%) compared with those with awareness (37.5%) (p<0.01). Regarding the mode of hospital arrival, patients arriving via private transport (84.47%) and ambulance (70.57%) had shorter delays than those referred by medical services (6.96%) (p<0.01). Patients with AF had a median delay of 3.99 hours, significantly lower than the 23.6 hours for patients without AF (p<0.01). Education level was also a significant factor, with patients having junior high school education or below experiencing a longer median delay (15.15 hours) compared with those with higher education (p=0.01). Patients with self-reported DM had a longer delay (22.38 hours) than patients without DM (11.99 hours) (p=0.01). Additionally, patients with weakness (63.57%) and consciousness disorder (1.88%) experienced longer delays (p<0.01) (table 1).
Multivariable analysis
In our study’s multivariable analysis, we unearthed pivotal independent predictors influencing healthcare seeking delays in AIS care among immigrants. Stroke awareness was strongly associated with delays, with individuals unaware of stroke symptoms being 4.29 times more likely to experience delays (aOR: 4.29, 95% CI: 2.83 to 6.49), suggesting that educational interventions could improve early recognition. Furthermore, wake-up strokes, characterised by the discovery of symptoms during sleep, were associated with a 4.37-fold increase in delays (aOR: 4.37, 95% CI: 2.28 to 8.36), suggesting challenges in timely recognition and response. The mode of hospital arrival also played a substantial role, with self-transporting patients encountering a 2.36-fold heightened risk of delay relative to those who used medical system referrals. Interestingly, the analysis revealed that patients with small artery occlusion strokes were more prone to delays, with a 2.05-fold increase, as opposed to those suffering from large artery atherosclerosis. Conversely, cardioembolic strokes were associated with fewer delays, with an aOR of 0.50 (95% CI: 0.28 to 0.90), possibly due to the severity or recognisability of symptoms expediting care seeking. Additionally, the presence of initial symptoms such as limb weakness, consciousness disturbances, dysarthria or aphasia was associated with a reduced risk of healthcare-seeking delay. Moreover, AF in patients with AIS was identified as a protective factor, with an aOR of 0.45 (95% CI: 0.23 to 0.89), underscoring the complexity of factors at play in the timely access to stroke care. Detailed insights and statistical analyses supporting these findings are thoroughly presented in table 2.
Table 2Multifactorial analysis of prehospital delays in the immigrant population among patients with acute ischaemic stroke
| Variable | Category | aOR (95% CI) | P value |
| Education level | High school vs junior high school and below | 0.99 (0.66 to 1.47) | 0.95 |
| Bachelor's degree and above vs junior high school and below | 0.54 (0.34 to 0.85) | 0.01 | |
| Stroke awareness | Unaware vs knowledgeable | 4.29 (2.99 to 6.16) | <0.01 |
| Smoking | No vs yes | 0.66 (0.45 to 0.96) | 0.03 |
| Private | Yes vs no | 2.36 (1.62 to 3.43) | <0.01 |
| Atrial fibrillation | Yes vs no | 0.45 (0.23 to 0.89) | 0.02 |
| Self-reported DM | Yes vs no | 2.30 (1.10 to 4.79) | 0.03 |
| TOAST classification | Small vessel stroke vs large vessel stroke | 2.05 (1.26 to 3.33) | 0.04 |
| Cardioembolic stroke vs large vessel stroke | 0.50 (0.28 to 0.90) | 0.02 | |
| Unexplained stroke vs. large vessel stroke | 1.07 (0.40 to 2.83) | 0.89 | |
| Other stroke vs large vessel stroke | 0.68 (0.45 to 1.04) | 0.08 | |
| Wake-up stroke | Yes vs no | 4.37 (2.28 to 8.36) | <0.01 |
| Weakness | Yes vs no | 0.42 (0.28 to 0.64) | <0.01 |
| Consciousness disorder | Yes vs no | 0.29 (0.12 to 0.68) | 0.01 |
| Dysarthria | Yes vs no | 0.48 (0.34 to 0.68) | <0.01 |
| Aphasia | Yes vs no | 0.13 (0.02 to 0.92) | 0.04 |
aOR, adjusted OR; DM, diabetes mellitus; TOAST, Trial of Org 10172 in Acute Stroke Treatment.
Analysis of healthcare-seeking delay among patients with different TOAST classifications
Violin plots offer a visual representation of healthcare-seeking delay times across various TOAST stroke classifications, uncovering significant differences (p=0.007) in how quickly patients sought medical attention. Patients with cardioembolic strokes tended to reach hospitals more swiftly on average than those in other categories. Conversely, individuals diagnosed with large-artery atherosclerosis strokes showed a broader spectrum of delay times, whereas those with small-vessel occlusions generally encountered longer delays, as depicted in figure 1.
Enlarge this image.Figure 1. Distribution of onset-to-door time (ODT) by TOAST stroke classifications. TOAST, Trial of Org 10172 in Acute Stroke Treatment.
The existence of notable disparities in healthcare-seeking delay rates and durations across the TOAST classifications, as detailed in table 2 and illustrated in figure 1, necessitated a comprehensive subgroup analysis. This analysis, presented in table 3, delves into the interplay between educational background and stroke type. It found that patients with lower education levels, specifically junior high school or below, predominantly fell within the large-artery atherosclerosis (41.4%) and cardioembolic stroke (47.14%) categories. In contrast, more educated patients, those holding a college degree or higher, were more susceptible to delays in the context of small-vessel strokes (24.16%). Additionally, within the cohort of patients with self-reported diabetes, a greater incidence of delays was associated with large-artery strokes (14.43%), as opposed to smaller proportions for small-vessel (7.87%) and cardioembolic strokes (1.43%). These nuanced findings highlight the complexity of factors associated with the timeliness of hospital arrival following stroke symptoms.
Table 3Subgroup analysis of healthcare-seeking delays by different TOAST classifications
| Variable | Category | Large artery stroke, n=388 (%) | Large artery stroke, n=356 (%) | Large artery stroke, n=70 (%) | P value |
| Age | / | 59.33±12.55 | 57.43±12.19 | 56.47±16.33 | 0.03 |
| Sex | Female | 295 (76.03) | 256 (71.91) | 45 (64.29) | 0.27 |
| Male | 93 (23.97) | 100 (28.09) | 25 (35.71) | ||
| Ethnicity | Han | 375 (96.65) | 347 (97.47) | 66 (94.29) | 0.51 |
| Others | 13 (3.35) | 9 (2.53) | 4 (5.71) | ||
| Living alone | No | 371 (95.62) | 350 (98.31) | 60 (85.71) | <0.01 |
| Yes | 17 (4.38) | 6 (1.69) | 10 (14.29) | ||
| Health insurance | No | 101 (26.03) | 89 (25.00) | 17 (24.29) | 0.89 |
| Yes | 287 (73.97) | 267 (75.00) | 53 (75.71) | ||
| Educational attainment | Junior high school and below | 164 (42.27) | 96 (26.97) | 33 (47.14) | <0.01 |
| High school | 166 (42.78) | 174 (48.88) | 29 (41.43) | ||
| Bachelor's degree and above | 58 (14.95) | 86 (24.16) | 8 (11.43) | ||
| Stroke awareness | Knowledgeable | 144 (37.11) | 133 (37.36) | 26 (37.14) | 0.39 |
| Unaware | 244 (62.89) | 223 (62.64) | 44 (62.86) | ||
| Wake-up stroke | No | 304 (78.35) | 305 (85.67) | 48 (68.57) | <0.01 |
| Yes | 84 (21.65) | 51 (14.33) | 22 (31.43) | ||
| Mode of arrival at the hospital | Transfer | 36 (9.28) | 15 (4.21) | 7 (10.00) | 0.02 |
| Private transport | 309 (79.64) | 322 (90.45) | 57 (81.43) | ||
| Ambulance | 43 (11.08) | 19 (5.34) | 6 (8.57) | ||
| Atrial fibrillation | No | 378 (97.42) | 353 (99.16) | 52 (74.29) | <0.01 |
| Yes | 10 (2.58) | 3 (0.84) | 18 (25.71) | ||
| NIHSS onset | 0–4 | 315 (81.19) | 341 (95.79) | 49 (70.00) | <0.01 |
| 5–20 | 60 (15.46) | 15 (4.21) | 14 (20.00) | ||
| 21–42 | 13 (3.35) | 0 (0.00) | 7 (10.00) | ||
| Self-reported DM | No | 332 (85.57) | 328 (92.13) | 69 (98.57) | 0.03 |
| Yes | 56 (14.43) | 28 (7.87) | 1 (1.43) | ||
| Drinking | No | 103 (26.55) | 75 (21.07) | 19 (27.14) | 0.03 |
| Yes | 285 (73.45) | 281 (78.93) | 51 (72.86) | ||
| Admission mRS | 0–2 | 100 (25.77) | 104 (29.21) | 10 (14.29) | <0.01 |
| 3–6 | 288 (74.23) | 252 (70.79) | 60 (85.71) | ||
| Recurrence of stroke | No | 297 (76.55) | 304 (85.39) | 60 (85.71) | 0.03 |
| Yes | 91 (23.45) | 52 (14.61) | 10 (14.29) | ||
| HBP | No | 88 (22.68) | 73 (20.51) | 25 (35.71) | <0.01 |
| Yes | 300 (77.32) | 283 (79.49) | 45 (64.29) | ||
| DM | No | 281 (72.42) | 291 (81.74) | 60 (85.71) | 0.013 |
| Yes | 107 (27.58) | 65 (18.26) | 10 (14.29) | ||
| Metabolic abnormalities | No | 315 (81.19) | 300 (84.27) | 65 (92.86) | 0.01 |
| Yes | 73 (18.81) | 56 (15.73) | 5 (7.14) | ||
| Dysarthria | No | 225 (57.99) | 249 (69.94) | 36 (51.43) | <0.01 |
| Yes | 163 (42.01) | 107 (30.06) | 34 (48.57) | ||
| Valvular heart disease | No | 382 (98.45) | 354 (99.44) | 58 (82.86) | <0.01 |
| Yes | 6 (1.55) | 2 (0.56) | 12 (17.14) |
(n=814).
DM, diabetes mellitus; HBP, High Blood Pressure; mRS, Modified Rankin Scale; NIHSS, National Institutes of Health Stroke Scale; TOAST, Trial of Org 10172 in Acute Stroke Treatment.
Discussion
This study observed that 82.6% of immigrant patients in Shenzhen experienced healthcare-seeking delays within the last 3 years, significantly higher than the average level of delay among patients with AIS in China. For instance, Zhou et al reported a 69.3% delay rate in Central China, while a multicentre study found a 75.2% delay rate in inland China.18 19 Moreover, regarding the duration of delay, our findings are consistent with previous research: a retrospective study in China involving 6102 patients who had a stroke showed a median onset-to-hospital arrival time of 15 hours (IQR 2.8–51 hours), with only about 1546 cases (25%) arriving within 3 hours and 2244 cases (37%) arriving after 6 hours.20 The immediacy of medical consultation for immigrant patients in our study is worse than in other countries. For example, research by Mkoma et al suggested that immigrant patients with AIS in Denmark arrive at the hospital later and have a higher delay rate than the native population, yet the median delay time reported in their study is substantially lower than what we have found.21 Despite urban development, medical accessibility for immigrant patients remains limited. These findings underscore the need to prioritise immigrant populations in public health strategies, with tailored interventions to address healthcare-seeking delays. In Shenzhen, the high proportion of immigrant workers with limited health literacy, coupled with economic pressures and a lack of familiarity with the healthcare system, likely contributes to these delays.18 Additionally, the rapid urbanisation and high patient volume may strain local healthcare infrastructure, further exacerbating delays for immigrant populations.20
Our investigation highlights a critical link between low educational attainment and increased healthcare-seeking delays in stroke treatment, affirming findings by Gonzalez-Aquines et al that only 22.1% of patients could identify AIS symptoms, a recognition rate much higher in individuals with greater educational achievements.22 This educational disparity is particularly evident among immigrants, as demonstrated by Lee et al, who observed that 74.03% of patients with an Onset-to-Door Time (ODT) exceeding 4.5 hours had less than 12 years of schooling, compared with 36.23% with more education.23 Furthermore, although our study did not find a statistically significant association between health insurance status and healthcare-seeking delays (p=0.2), other studies suggest that lack of health insurance may exacerbate delays by creating financial barriers to accessing timely care. For instance, uninsured individuals may delay seeking care due to concerns about out-of-pocket costs, while insured patients may benefit from referral systems that facilitate quicker access to emergency services.20 These findings highlight the need to investigate the nuanced role of health insurance in mitigating healthcare-seeking delays, especially within underserved populations like immigrants. Given these disparities, there’s a pressing need for targeted educational initiatives aimed at improving literacy among lower-educated immigrants, who significantly contribute to treatment delays due to various barriers, including economic constraints and fear of job loss. Enhanced public health education, coupled with policy adjustments and community-based interventions, is vital for ensuring timely access to stroke care, especially in light of comparative analyses that reveal immigrants face more pronounced delays than their non-immigrant counterparts. This unified approach could not only empower individuals to seek immediate care, thereby potentially reducing the burden of delayed treatment on healthcare systems, but also bridge the gap in stroke outcome disparities.
Our study delineates stark disparities in healthcare seeking delays among patients with small vessel occlusion (SVO) versus those with cardioembolic strokes, revealing a pivotal need for enhanced public education and healthcare system improvements. Previous research has similarly found that SVO strokes are characterised by less severe symptoms such as headache, dizziness and blurred vision, which may lead to misunderstandings and subsequent delays in medical intervention.24 This contrasts with the more acute symptoms of cardioembolic strokes, such as hemiplegia and aphasia, which typically prompt immediate healthcare-seeking. Despite SVO strokes' potentially milder course due to minor arterial lesions, the delay in seeking treatment compromises patient outcomes, illustrating a critical paradox where seemingly benign symptoms can result in significant healthcare challenges.25 Moreover, the limited use of thrombolytic therapy, restricted by tight treatment windows, underscores the urgency for early hospital presentation.26 Addressing this issue may benefit from a comprehensive strategy that combines public education to highlight the potential importance of 'minor' stroke symptoms with healthcare reforms aimed at improving the efficiency of stroke diagnosis and treatment. Such initiatives are crucial for reducing healthcare seeking delays and improving overall stroke care outcomes, directly impacting the quality of patient care and healthcare resource utilisation. Furthermore, the disparities in delay across TOAST classifications could be influenced by differences in the two phases of delay. For example, the milder symptoms of SVO strokes, such as dizziness and blurred vision, are more likely to contribute to decision-making delays, as patients may not recognise the urgency of their symptoms. Conversely, the severe symptoms of cardioembolic strokes, such as aphasia and hemiplegia, might reduce decision-making delays but could still be affected by transportation delays, especially in patients not using EMS.27
Our analysis highlights a troubling trend among stroke patients, particularly smokers and those choosing self-transportation to hospitals, marked by significant delays in seeking medical care. This delay may be associated with a general underestimation of the severity of stroke, particularly among smokers. Delays have been previously reported.11 This may be because their optimistic bias toward health risks and lack of self-discipline lead to worse stroke outcomes.28 The preference for self-transport, spurred by urban transportation convenience and economic considerations, undermines the critical role of EMS in reducing ODT and improving stroke prognosis.29 Indeed, EMS has been shown to decrease ODT to an average of 151 min, significantly enhancing the likelihood of hospital arrival within the vital 3-hour window to 54%.30 These findings suggest the potential importance of enhancing public health education on stroke symptom recognition and the benefits of timely EMS use, as well as the optimisation of healthcare resource allocation and facility planning. Such measures are vital for addressing healthcare-seeking delays and elevating the quality of stroke care outcomes, highlighting the need for integrated strategies that combine education, resource optimisation, and healthcare planning to tackle the challenges identified in our study.
Wake-up strokes, occurring during sleep and representing 14–27% of AIS,31–33 underscore a unique challenge in stroke management due to the indeterminate time of onset, leading to significant treatment delays. Particularly affected are patients with large vessel strokes, where severe symptoms like impaired consciousness and aphasia hinder the ability to seek swift medical consultation. Additionally, the timely initiation of treatment is further complicated by the misinterpretation of milder stroke symptoms, commonly observed in minor strokes or among patients with self-reported diabetes, who are 2.3 times more likely to experience delays.34 These delays are exacerbated by factors such as the absence of family members during early hours and communication barriers in patients with certain neurological impairments. Given these complexities, a concerted effort for public and healthcare professional education is essential, focusing on early symptom recognition and diabetes management to mitigate healthcare-seeking delays. This approach not only addresses the unique challenges posed by wake-up strokes but also highlights the broader public health implications of stroke awareness, advocating for strategies that enhance early detection and facilitate prompt, effective treatment.
This study did not separately analyse decision-making and transportation delays due to dataset limitations. Future research should incorporate detailed time metrics to better understand and address the specific components of healthcare-seeking delays. Additionally, this study lacks a reference group of non-migrants, which limits the ability to determine whether the observed delays are specific to the migrant population or are also prevalent in the general population. Future research should include non-migrant control groups to better contextualise these findings and clarify the unique challenges faced by migrant patients.
Conclusion
Significant delays in healthcare seeking for stroke among immigrants highlight significant gaps in timely access to treatment. Increased focus on immigrant patients, as well as improved public health education and system reforms to address emergency service utilisation. Awareness of stroke and misunderstanding of symptoms, especially in patients with comorbidities, require targeted educational programmes. Strategic interventions are needed to bridge disparities in stroke knowledge and access to care, emphasising the importance of community and healthcare provider collaboration. This call to action aims to improve stroke outcomes through comprehensive education, policy reform and healthcare improvements.
We sincerely thank Shenzhen Longhua District People's Hospital for supporting this study and all members of the Department of Neurology for their help.
Data availability statement
Data are available upon reasonable request. The data supporting the findings of this study are available from the corresponding author ([email protected]) upon reasonable request.
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
This study was conducted in accordance with the ethical standards of the 1964 Helsinki Declaration and its later amendments. Given the retrospective nature of our research and the use of anonymised data, the Ethics Review Committee of Shenzhen Longhua District People’s Hospital granted an exemption from full review (Approval Number: 2024073). Prior to data extraction from the hospital's electronic information system, complete anonymisation of the data ensured the privacy protection of participants, making it impossible to trace back to individual participants.
Contributors CL conceived the study, coordinated data collection, and drafted the manuscript. ZW conducted statistical analysis and reviewed the manuscript. YJ contributed to data validation and interpretation. YL contributed to study design and provided clinical expertise. QZ supervised the project and is the guarantor for the overall content of the manuscript. All authors approved the final manuscript. Use AI to help polish the language of manuscripts and proofread capitalisation errors.
Funding This study was funded by the Scientific Research Projects of Medical and Health Institutions of Longhua District, Shenzhen, under grant number 2024046.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.
Provenance and peer-review Not commissioned; externally peer-reviewed.
1 Kleindorfer DO, Towfighi A, Chaturvedi S, et al. 2021 Guideline for the Prevention of Stroke in Patients With Stroke and Transient Ischemic Attack: A Guideline From the American Heart Association/American Stroke Association. Stroke 2021; 52: e364–467. doi:10.1161/STR.0000000000000375
2 Zhou M, Wang H, Zeng X, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 2019; 394: 1145–58. doi:10.1016/S0140-6736(19)30427-1
3 Bhatia K, Bhagavan S, Bains N, et al. Current Endovascular Treatment of Acute Ischemic Stroke. Mo Med 2020; 117: 480–9.
4 Bushnell C, Kernan WN, Sharrief AZ, et al. 2024 Guideline for the Primary Prevention of Stroke: A Guideline From the American Heart Association/American Stroke Association. Stroke 2024; 55: e344–424. doi:10.1161/STR.0000000000000475
5 Ebinger M, Siegerink B, Kunz A, et al. Association Between Dispatch of Mobile Stroke Units and Functional Outcomes Among Patients With Acute Ischemic Stroke in Berlin. JAMA 2021; 325: 454–66. doi:10.1001/jama.2020.26345
6 Prabhakaran S, Ruff I, Bernstein RA. Acute stroke intervention: a systematic review. JAMA 2015; 313: 1451–62. doi:10.1001/jama.2015.3058
7 Chao B-H, Yan F, Hua Y, et al. Stroke prevention and control system in China: CSPPC-Stroke Program. Int J Stroke 2021; 16: 265–72. doi:10.1177/1747493020913557
8 Grech P, Grech R. The role of health promotion theories in Stroke Awareness and Education. Appl Nurs Res 2021; 58: 151415. doi:10.1016/j.apnr.2021.151415
9 Wang Y, Liao X, Zhao X, et al. Using recombinant tissue plasminogen activator to treat acute ischemic stroke in China: analysis of the results from the Chinese National Stroke Registry (CNSR). Stroke 2011; 42: 1658–64. doi:10.1161/STROKEAHA.110.604249
10 Jiang Y, Xiong Y, Chi Y, et al. Healthcare-Seeking Delays in Acute Ischemic Stroke Patients: The Influence of Gender, Immigrant Status, and Educational Background. Risk Manag Healthc Policy 2024; 17: 191–204. doi:10.2147/RMHP.S445001
11 Zhu Y, Zhang X, You S, et al. Factors Associated with Pre-Hospital Delay and Intravenous Thrombolysis in China. J Stroke Cerebrovasc Dis 2020; 29: 104897. doi:10.1016/j.jstrokecerebrovasdis.2020.104897
12 Gu S, Dai Z, Shen H, et al. Delayed Stroke Treatment during COVID-19 Pandemic in China. Cerebrovasc Dis 2021; 50: 715–21. doi:10.1159/000517075
13 Terecoasă EO, Radu RA, Negrilă A, et al. 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: 1003. doi:10.3390/medicina58081003
14 Emberson J, Lees KR, Lyden P, et al. Effect of treatment delay, age, and stroke severity on the effects of intravenous thrombolysis with alteplase for acute ischaemic stroke: a meta-analysis of individual patient data from randomised trials. The Lancet 2014; 384: 1929–35. doi:10.1016/S0140-6736(14)60584-5
15 Hu W, Li L, Su M. Spatial Inequity of Multi-Level Healthcare Services in a Rapid Expanding Immigrant City of China: A Case Study of Shenzhen. Int J Environ Res Public Health 2019; 16: 3441. doi:10.3390/ijerph16183441
16 Bureau Of Statistics Of Shenzhen Municipality, National Bureau Of Statistics Of The People’S Republic Of China Shenzhen Investigation Team. Shenzhen Statistical Yearbook 2023. 2023.
17 Schaefer PW, Huisman TAGM, Sorensen AG, et al. Diffusion-weighted MR imaging in closed head injury: high correlation with initial glasgow coma scale score and score on modified Rankin scale at discharge. Radiology 2004; 233: 58–66. doi:10.1148/radiol.2323031173
18 Zhou Y, Yang T, Gong Y, et al. Pre-hospital Delay after Acute Ischemic Stroke in Central Urban China: Prevalence and Risk Factors. Mol Neurobiol 2017; 54: 3007–16. doi:10.1007/s12035-016-9750-4
19 Jiang B, Ru X, Sun H, et al. Pre-hospital delay and its associated factors in first-ever stroke registered in communities from three cities in China. Sci Rep 2016; 6. doi:10.1038/srep29795
20 Jin H, Zhu S, Wei JW, et al. Factors associated with prehospital delays in the presentation of acute stroke in urban China. Stroke 2012; 43: 362–70. doi:10.1161/STROKEAHA.111.623512
21 Mkoma GF, Norredam M, Iversen HK, et al. Use of reperfusion therapy and time delay in patients with ischaemic stroke by immigration status: A register-based cohort study in Denmark. Eur J Neurol 2022; 29: 1952–62. doi:10.1111/ene.15303
22 Gonzalez-Aquines A, Cordero-Pérez AC, Cristobal-Niño M, et al. Contribution of Onset-to-Alarm Time to Prehospital Delay in Patients with Ischemic Stroke. J Stroke Cerebrovasc Dis 2019; 28: 104331. doi:10.1016/j.jstrokecerebrovasdis.2019.104331
23 Lee E-J, Kim SJ, Bae J, et al. Impact of onset-to-door time on outcomes and factors associated with late hospital arrival in patients with acute ischemic stroke. PLoS ONE 2021; 16: e0247829. doi:10.1371/journal.pone.0247829
24 Jiang Y, Zhao Q, Guan J, et al. Analyzing prehospital delays in recurrent acute ischemic stroke: Insights from interpretable machine learning. Pat Educ Couns 2024; 123: 108228. doi:10.1016/j.pec.2024.108228
25 Mainz J, Andersen G, Valentin JB, et al. Treatment Delays and Chance of Reperfusion Therapy in Patients with Acute Stroke: A Danish Nationwide Study. Cerebrovasc Dis 2023; 52: 275–82. doi:10.1159/000526733
26 Wang T, Xu J, Wang A, et al. Night-time diastolic blood pressure variability relates to stroke recurrence in patients who had ischaemic stroke with small artery occlusion. Stroke Vasc Neurol 2022; 7: 237–44. doi:10.1136/svn-2020-000718
27 Grefkes C, Fink GR. Recovery from stroke: current concepts and future perspectives. Neurol Res Pract 2020; 2: 17. doi:10.1186/s42466-020-00060-6
28 Noubiap JJ, Fitzgerald JL, Gallagher C, et al. Rates, Predictors, and Impact of Smoking Cessation after Stroke or Transient Ischemic Attack: A Systematic Review and Meta-Analysis. J Stroke Cerebrovasc Dis 2021; 30: 106012. doi:10.1016/j.jstrokecerebrovasdis.2021.106012
29 Sangari A, Akhoundzadeh K, Vahedian M. Prehospital notification reduced time delays but not neurological outcomes in acute ischemic stroke. In Review [ Preprint ]. doi:10.21203/rs.3.rs-440192/v1
30 Rossnagel K, Jungehülsing GJ, Nolte CH, et al. Out-of-hospital delays in patients with acute stroke. Ann Emerg Med 2004; 44: 476–83. doi:10.1016/j.annemergmed.2004.06.019
31 Rimmele DL, Thomalla G. Wake-up stroke: clinical characteristics, imaging findings, and treatment option - an update. Front Neurol 2014; 5: 35. doi:10.3389/fneur.2014.00035
32 Nagai K, Aoki J, Sakamoto Y, et al. About 30% of wake-up stroke patients may be candidate for the tPA therapy using Negative-FLAIR as a “tissue clock”. J Neurol Sci 2017; 382: 101–4. doi:10.1016/j.jns.2017.09.042
33 Thomalla G, Fiebach JB, Østergaard L, et al. A Multicenter, Randomized, Double-Blind, Placebo-Controlled Trial to Test Efficacy and Safety of Magnetic Resonance Imaging-Based Thrombolysis in Wake-up Stroke (WAKE-UP). Int J Stroke 2014; 9: 829–36. doi:10.1111/ijs.12011
34 Nepal G, Yadav JK, Basnet B, et al. Status of prehospital delay and intravenous thrombolysis in the management of acute ischemic stroke in Nepal. BMC Neurol 2019; 19: 155. doi:10.1186/s12883-019-1378-3
© 2025 Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group. http://creativecommons.org/licenses/by-nc/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ . Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Zhao, Qingshi 1 ; Li, Yanfeng 1 1 Department of Neurology, The People's Hospital of Longhua, Shenzhen, Guangdong, China
2 Nursing Department, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, Guangdong, China