Use of Mechanical Thrombectomy versus Tissue Plasminogen Activator in Low–Middle-Income Countries: A Meta-Epidemiological Analysis of Treatment Options and Social Determinants of Health

Abstract

Introduction

Acute ischemic stroke (AIS) is the second leading cause of death worldwide, and the disease burden falls greatly on low- and middle-income countries (LMICs) with around 63% of the global incidence. Despite medical advancements in high-income countries, this disparity in outcomes persists in LMICs. The impact of socioeconomic factors has not been well explored on treatment accessibility for AIS.

Methods

We completed a systematic review using PubMed, Scopus, and Web of Science with the search focused on outcomes of AIS in LMICs. A pooled meta-analysis was completed with these studies correlating variables to the use of tissue plasminogen activator (TPA) and mechanical thrombectomy (MT) treatment.

Results

About 46 studies were found for the use of TPA or MT for AIS in LMICs between 2007 and 2022. The percentage of the population living below 50% of the median income correlated with TPA use. MT use correlated with the median income, population density, average life expectancy, poverty–headcount ratio, and gross national income (GNI) per capita. Meanwhile, we found that both TPA and MT use insignificantly correlated with better in-hospital mortality and poor neurological outcome rates.

Conclusion

This meta-regression analysis uniquely explores the relationship between socioeconomic parameters in LMICs and the use of specific AIS therapies. Country parameters related to wealth disparity and population density correlated with treatment use. Further research with data involving delay to treatment and the potential impact of dedicated stroke centers warranted investigating the role of treatment accessibility in improving AIS outcomes.

 

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Keywords

acute ischemic stroke - low- and middle-income countries - social determinants of health - mechanical thrombectomy - tissue plasminogen activator

Introduction

Strokes are the second most common cause of death and have the third highest disability-adjusted life-years (DALYs) lost, worldwide.[1] Acute ischemic strokes (AISs) are more common than hemorrhagic and can occur when a cerebral blood vessel is occluded, leading to diminished tissue perfusion and eventual infarction with neuronal loss.[2] Vessel occlusions can be attributed to embolisms and large or small vessel diseases, among other mechanisms. In 2019, the global prevalence of AIS was determined to be 77 million with an incidence rate of approximately 4.7 million new cases per year.[1] The mortality rate was determined to be 3.3 million cases per year, with a loss of more than 63 million years of healthy life per year. The impact of this disease is especially pronounced in low- and middle-income countries (LMICs), which experience the majority of the stroke burden, with 86% of the stroke-related deaths and 89% of the stroke-related DALYs.[1] In addition to a higher prevalence, LMICs lack the amount of access to neurosurgical care that you see in high-income countries (HICs).[3] These disparities are also influenced by social determinants of health (SDOH), which have been shown to impact stroke outcomes in the United States and are a factor in other neurosurgical conditions in LMICs.[4] [5] [6] [7] In the United States, median household income, access to parks, and female head of the household significantly correlated with stroke outcomes, and the suggested mechanism for this is the ability to adhere to medication and lifestyle changes to improve comorbidities.[5] [6] Treatment for AIS has developed significantly in HICs with the approval of tissue plasminogen activator (TPA), in which a medication is administered to dissolve the clot, and mechanical thrombectomy (MT), where endovascular surgery is used to mechanically remove the clot.[8] [9] However, it is not well established whether these treatments have become accessible or improved the outcomes after AIS in LMICs.[8] [9] Although there are policies and guidelines, such as improving community engagement and access to designated stroke facilities,[5] to reduce disparity of care between LMICs and HICs, there are barriers and challenges to delivering these interventions globally. This study explores the use of TPA and MT in LMICs and investigates how SDOH may correlate with access to these treatments. Additionally, it compares overall outcomes following AIS to the use of these treatments and suggests how this information can be applied to future interventions.

 

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Methods

Search Strategy for Meta-Analysis and Quality Assessment

A systematic review was conducted as part of an established protocol (PROSPERO ID: CRD42023404915) and according to the 2020 Preferred Reporting in Systematic Reviews and Meta-Analyses (PRISMA) guidelines.[10] Using PubMed, Web of Science, and Scopus for publication databases, a full search query using Medical Subject Headings (MesH) and non-MesH terms related to ischemic strokes and LMICs is summarized in [Supplementary Table S1] (available in the online version only). Some of these terms included “ischemic stroke” and “mortality.” This search was completed in September 2023 and had no time frame limitations. Studies were included if they discussed the use of TPA or MT for AIS in their project and if they reported the mortality and/or the neurological deficits following AIS in LMICs, where LMICs were identified as defined by the World Bank and used in other studies.[11] Systematic reviews, meta-analyses, and other literature reviews were excluded but were used for citation matching based on the inclusion criteria. Publications underwent title, abstract, and full-text screens by two authors, A.R.L. and M.E. Disagreements were settled by a third author, A.N. Each included study was assessed using the Newcastle–Ottawa Scale (NOS) to ascertain sources of bias in each study.[12] In concordance with the literature, scores of 7 to 9 were considered high quality, 5 to 6 were considered moderate quality, and ≤4 were considered low quality. The quality assessments were completed by a single author, A.R.L., to minimize discrepancies in grading.

 

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Outcomes Assessed and Other Data Sources

The primary outcomes assessed were the use of TPA or MT in AIS. Additionally, data regarding in-hospital mortality and neurological deficit were collected. Two authors, A.R.L. and M.E., collected and verified the extracted data from each study. Other parameters included and compared include hospital setting (rural or urban), patient population (pediatrics defined as ≤18 years old or adult), and treatment (TPA or MT). Using the World Bank Open Data database, we noted the country that each study was completed in and queried country-specific parameters, including median income, life expectancy, population density, gross domestic product (GDP), statistical capacity score—a metric developed by the World Bank to estimate the reliability and timeliness of data produced, the percentage of residents below the global poverty threshold, gross national income (GNI) per capita, and school enrollment. The statistical capacity score in this study gives insight into the quality of the collected data; however, it was not significantly correlated with outcomes, indicating that there was not a strong variation in this measure and the results of this study.

 

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Statistical Analysis

The prevalence of AIS was adjusted based on the other parameter. The extracted data were pooled using the inverse-variance method and a random-effect model given the heterogeneity observed using the DerSimonian–Laird heterogeneity estimator. The random-effect model captures the uncertainty of the variables when there is high heterogeneity in the data. Effects of hospital environment and study population were accounted for using a mixed-effects meta-regression model, which incorporates heterogeneity and group sizes into the analysis. A proportional meta-analysis was completed to calculate the adjusted prevalence. The next step in this study was to compare in-hospital mortality and neurological deficit based on the treatment protocol for each study between TPA and MT. A univariate analysis was completed to compare the correlation between treatment type with in-hospital mortality and neurological deficit. Finally, we compared the accessibility of TPA and/or MT based on country demographics and economic statistics as mentioned previously. The impact of these parameters on the use of TPA or MT was analyzed using univariate and multivariate analyses to estimate the correlation of the parameter with the treatment protocol. Univariate analysis was completed using chi-square tests for qualitative variables or analysis of variance (ANOVA) for quantitative variables, and a multivariate linear regression was completed for multivariate analysis. Data are reported as mean ± standard deviation (SD) or percentages for continuous variables and proportions for categorical variables. Both parametric and non-parametric methods were performed for each hypothesis test, if possible. All tests were two-sided with statistical significance defined as p < 0.05. The analysis was completed in R (R version 4.4.2) using the dmetar and meta packages.

 

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Results

Included Studies

After the search, 765 studies were queried and 219 underwent full-text screening. After final selection, 154 articles were included for the final analysis ([Fig. 1]). Of those, there were 46 publications, representing 14,910 participants, that reported the use of MT or TPA for treatment. About 26 studies were considered high quality based on the NOS assessment, while 1 was fair and 19 were of poor quality. No study was excluded based on the assessment score. A summary of the included studies and quality assessment scores is available in [Supplementary Table S2] (available in the online version). The cohorts of these studies spanned 13 different countries, with India being the most represented with 14 studies. Only 4 studies (8.7%) were completed in rural hospitals. All the studies included only adult patients, and the average age of participants within each study ranged from 35 to 69 years. About 34.3% of the participants were female. Overall, 2,474 (16.6%) received TPA while 1,247 (8.4%) received MT.

Fig. 1 PRISMA diagram of included studies. PRISMA.

 

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Pooled Summary

The pooled rate, using the random-effects model, for use of TPA was 0.20 (0.01, 0.80; [Fig. 2]) and 0.01 (0.00, 0.35) for MT ([Fig. 3]), although there was a significant amount of heterogeneity in the reported use of both treatments. The pooled proportion of each treatment by country is reported in [Table 1]. The lowest rates of TPA use were in Zambia, Ethiopia, Burkina Faso, Benin, and Tanzania, which did not report any use of TPA, while the highest was 100% of reported cases in Pakistan and Nepal. The lowest rates of MT use were in Pakistan, Zambia, Ethiopia, Burkina Faso, Benin, and Tanzania (0%), while the highest was in Vietnam (99.7%).

Fig. 2 Forest plot for TPA use in each study. The box of each study represents that reported proportion while the line represents the error based on the pooled proportion model. The image to the right of the random-effects model represents the error. TPA, tissue plasminogen activator.

Fig. 3 Forest plot for MT use in each study. The box of each study represents that reported proportion while the line represents the error based on the pooled proportion model. The image to the right of the random-effects model represents the error. MT, mechanical thrombectomy.

Table 1 Pooled proportions of tissue plasminogen activator and mechanical thrombectomy use by country

Country	Tissue plasminogen activator	Mechanical thrombectomy
Pakistan	1.0000 [0.0000, 1.0000]	0.0000 [0.0000, 1.0000]
India	0.4217 [0.0559, 0.8998]	0.1811 [0.0081, 0.8568]
Morocco	0.0282 [0.0212, 0.0374]	0.1918 [0.1734, 0.2116]
Iran	0.3424 [0.0000, 1.0000]	0.0009 [0.0000, 0.2233]
Egypt	0.9998 [0.0038, 1.0000]	0.3914 [0.0000, 1.0000]
Nepal	1.0000 [0.0000, 1.0000]	NA
Vietnam	0.0700 [0.0025, 0.6898]	0.9974 [0.0006, 1.0000]
Zambia	0.0000 [0.0000, 1.0000]	0.0000 [0.0000, 1.0000]
Philippines	0.0690 [0.0173, 0.2375]	NA
Ethiopia	0.0000 [0.0000, 1.0000]	0.0000 [0.0000, 1.0000]
Burkina Faso	0.0000 [0.0000, 1.0000]	0.0000 [0.0000, 1.0000]
Benin	0.0000 [0.0000, 1.0000]	0.0000 [0.0000, 1.0000]
Tanzania	0.0000 [0.0000, 1.0000]	0.0000 [0.0000, 1.0000]

Reported as proportion [CI].

 

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Meta-Regression Analysis

A pooled meta-analysis was completed for the use of TPA and MT using univariate and multivariate meta-regression analyses. Factors, such as patient demographics and socioeconomic measures for the country of the study, were included in this analysis to evaluate their correlation with the use of each treatment. The results of each analysis are reported in [Tables 2] and [3].

Table 2 Regression analysis for factors correlated with tissue plasminogen activator use

	Univariate			Multivariate
	Beta	SE	p-Value	Beta	SE	p-Value
Study cohort
Patient age	−0.8693	0.4322	0.0443	0.4843	1.208	0.6885
Study year	−0.7519	0.4279	0.0789	–
Percentage of females	4.7803	11.6584	0.6818	–
Comorbidities
Percentage of Hypertension (HTN)	21.4334	15.2414	0.1596	–
Percentage of diabetes	22.5932	14.579	0.1212	–
Percentage of Atrial Fibrillation (Afib)	−7.5093	37.6667	0.842	–
Percentage of Anti-platelet/Anti-coagulant (APAC)	48.3482	25.992	0.0629	–
Country parameters
GDP	0	0	0.3006	–
Median income	0.0035	0.002	0.0818	–
Percentageliving below 50% of the median income	−1.6885	0.6301	0.0074	−1.985	1.0065	0.0486
Cost of living	−0.333	0.1757	0.058	–
Access to health care metric	0.0783	0.1251	0.5313	–
Population density	0.0103	0.0085	0.225	–
Life expectancy	0.5568	0.391	0.1545	–
Poverty–headcount ratio	−0.1378	0.1572	0.3807	–
School enrollment	0.0767	0.1853	0.6791	–
GNI	0.0025	0.0018	0.1494	–

Abbreviation: GNI, gross national income; MT, mechanical thrombectomy.

Table 3 Regression analysis for factors correlated with mechanical thrombectomy use

	Univariate			Multivariate
	Beta	SE	p-Value	Beta	SE	p-Value
Study cohort
Patient age	0.1412	0.6696	0.833	–
Study year	0.9879	0.6437	0.1249	–
Percentage of females	−9.8025	15.1049	0.5164	–
Comorbidities
Percentage of HTN	−15.4694	12.1592	0.2033	–
Percentage of diabetes	13.0569	8.8583	0.1405	–
Percentage of Afib	39.1574	19.102	0.0404	36.1764	12.4587	0.0037
Percentage of APAC	22.6005	18.925	0.2324	–
Country parameters
GDP	0	0	0.1383	–
Median income	0.0045	0.0016	0.0047	–
Percentage living below 50% of the median income	−0.0993	0.4929	0.8403	–
Cost of living	−0.3257	0.1985	0.1009	–
Access to health care metric	0.2115	0.1624	0.1929	–
Population density	0.0257	0.0109	0.0178	0.0394	0.0137	0.0041
Life expectancy	0.7991	0.3609	0.0268	–
Poverty–headcount ratio	−0.4176	0.1695	0.0137	–
School enrollment	1.1658	0.7635	0.1268	–
GNI	0.004	0.0014	0.0047	0.0046	0.002	0.0202

Abbreviation: GNI, gross national income.

We also completed a univariate meta-regression analysis to evaluate the relationship between the use of TPA or MT and patient outcomes, including in-hospital mortality and poor neurological outcomes. The results are demonstrated in [Figs. 4] and [5]. They show that, in the univariate analysis, patient age and the percentage of the country's population living below 50% of the median income significantly correlate with the rate of TPA use for AIS. The percentage of the country's population living below 50% of the median income, which represents economic disparity, negatively correlates with TPA use while mean patient age correlates positively. While the proportion of patients with atrial fibrillation, the country's median income, the population density, the average life expectancy, the poverty–headcount ratio, and the GNI per capita significantly correlate with MT use in the univariate analysis. Each of these factors, except for the poverty–headcount ratio, positively correlates with the MT rate.

Fig. 4 Map indicating the proportion of MT or TPA use by country. MT, mechanical thrombectomy; TPA, tissue plasminogen activator.

Fig. 5 Correlation between proportion of patients treated with MT or TPA and overall in-hospital mortality or poor neurological outcome rates. MT, mechanical thrombectomy; TPA, tissue plasminogen activator.

For TPA multivariate analysis, both significant factors were included, and only the percentage of the country's population living below 50% of the median income remained significantly, negatively correlated with TPA use. There was a strong collinearity between median income, poverty–headcount ratio, life expectancy, and GNI per capita in the MT analysis. Therefore, only GNI per capita, population density, and proportion with atrial fibrillation were included in the final multivariate model for MT use ([Table 3]). These parameters were selected based on their significance values and their relationship with other variables. Each of these factors was significantly positively correlated with the rate of MT treatment.

 

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Analysis of Treatment Use and Patient Outcomes

Univariate analysis was completed to evaluate the relationship between rates of TPA or MT use and in-hospital mortality or poor neurological outcomes following AISs ([Fig. 5]). In each of these analyses, the rate of TPA use was negatively correlated with the in-hospital mortality rate and the rate of poor neurological outcomes; however, this was non-significant. Similarly, the rate of MT use was insignificantly, negatively correlated with the in-hospital mortality rate and the rate of poor neurological outcomes.

 

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Discussion

Relationship between Treatment Use and Social Determinants of Health

It is well established that LMICs have a larger burden of disease, often attributed to access to care and the availability of certain treatment options.[13] Previous studies have demonstrated the relationship between SDOH and neurological care using traumatic brain and spinal cord injury data.[7] [14] As well as in HICs, SDOH measures have been shown to correlate with worse outcomes following a stroke.[6] This meta-regression aims to demonstrate the impact of socioeconomic factors in the treatment of AIS in LMICs as well. This analysis shows that the socioeconomic factors of the country were significantly associated with the use of TPA or MT in treatment.

For the univariate and multivariate analyses of TPA use, the percentage of the population living below 50% of the median income negatively correlated with TPA use. This socioeconomic measure increases with greater economic disparity within the country. Ultimately, this means that as economic disparity increased, the use of TPA for AIS decreased. In HICs, low socioeconomic status was associated with delay in treatment[15] and decreased likelihood to seek care.[16] Our study correlates with these findings since, as a larger proportion of the country falls within the lower socioeconomic range, there will likely be a greater delay in care, resulting in a reduced ability to use TPA. Additionally, this population may be more likely to receive care at low-resource hospitals that may not have access to these therapies. In the univariate analysis for MT use, several SDOH measures correlated significantly with this therapy, including median income, population density, average life expectancy, poverty–headcount ratio, and GNI per capita. Of these, only the poverty–headcount ratio negatively correlated with use, which indicates that as a larger proportion of the country falls under the poverty income level, the rate of MT for AIS reduces. Whereas an increase in median income, GNI per capita, and life expectancy correlated with increased MT use. This may be the consequence of better access to medical care overall. As expected, GNI per capita of the country correlates significantly with the median income, poverty–headcount ratio, and average life expectancy; therefore, only GNI per capita was included in the final multivariate analysis along with population density as the other SDOH measure. Both variables were significantly positively correlated with MT use. As we found in LMICs, the relationship between increased economic standings and access to MT has been previously shown across HICs.[17] The population density result may indicate that countries with a higher population density have greater access to health care centers and access to modern therapies. Alternatively, denser locations may have a reduced delay in treatment or more experience with these treatments, allowing them to use them on a greater proportion of the patients. The impact of population density has been discussed in HICs, such as New Zealand, which suggested that scarcer regions suffer mostly from decreased access to stroke centers.[18] [19]

The SDOH measures were more often associated with treatment use than patient demographics. Our analysis for TPA use shows that in the univariate analysis, as mean patient age increased, the use of TPA decreased. This may have been due to a concern for complications in these older patients. Alternatively, since younger patients are more likely to have a stroke in LMICs,[20] physicians may be more familiar with the treatment of TPA in this population. When accounting for economic disparity in the multivariate analysis, the mean patient age was insignificantly positively correlated with TPA use. The explanation behind this may be associated with the increased wealth of the older patients in these countries with high economic disparity, allowing for improved access to health care and modern therapies. These populations may also be more likely to live near health care centers, allowing for an earlier presentation of AIS. In MT treatment, a higher proportion of patients with atrial fibrillation correlated with a higher use of MT in both the univariate and multivariate analyses. This may be related to patients with a known diagnosis of atrial fibrillation having increased access to care or residing in locations with nearby hospitals. Another possible explanation for the location of the ischemic stroke may vary in patients with atrial fibrillation and may be more receptive to MT treatment.

The relationship between SDOH and treatment types suggests that a barrier to improving stroke care in LMICs is access to modern treatments. Targeting this aspect of neurological care in these countries may be an essential step to reducing the disparity we see in disease burden and mortality between LMICs and HICs.

 

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Implications for Clinical Practice

In 2017, the global incidence of AIS was 101.3 (91–113.6) per 100,000,[21] with most of the disease burden falling on LMICs.[22] Additionally, the mortality rate for AISs in LMIC is almost double the rate in HICs, such as the United States (3.1% according to a study by the National Inpatient Sample) and Germany (6.7%).[23] [24] This disparity in patient outcomes could be the result of access to modern treatments, including TPA and MT, or the time between symptom onset and treatment. Overall, we found that the use of MT in the meta-analysis (1%) was less than the global median rate of 2.79%.[17] However, the use of TPA (20%) was substantially higher than the 3% that has been reported in HICs, such as the United States and New Zealand.[9] [19] These studies found that up to 40% of patients could have appropriately been treated with TPA,[9] [19] so this finding may be a result of caution in HICs or opting to use MT alone instead. Additionally, we show that TPA and MT treatments may influence overall in-hospital mortality and the rate of poor neurological outcomes. Although these analyses were insignificant, the trend indicated that higher rates of TPA and MT use were associated with lower rates of in-hospital mortality and poor neurological outcomes. These findings may be a result of delayed treatment to care or a scarcity of dedicated stroke centers in the countries. In HICs, the use of MT for AIS has had variable results on patient outcomes, especially regarding mortality.[25] [26] [27] This variability between studies may be reflected in this study as the insignificance of these relationships between treatments and outcomes. Patient outcomes in this study may also be impacted by a delay in treatment. It is generally accepted that this delay in treatment in AIS is associated with worse clinical outcomes. In HICs, there has been a major effort to improve the general population's awareness of stroke symptoms with a goal of minimizing the overall time to treatment, as this is regarded as an essential factor in positive outcomes.[28] In LMICs, the delay in treatment for AIS may be even greater and out of the public's control due to reduced availability of neurological care overall.[29] There has been a decline in the incidence of AIS in LMICs over the last 30 years, but the rate of poor neurological outcomes in this population has increased.[30] One plausible explanation for an increase in poor neurological outcomes is that a greater number of patients are presenting to the hospital, but the delay in treatment for a larger proportion of patients contributes to a greater rate of poor outcomes. One recommendation based on a review of protocols in LMICs from Prust et al. suggests the construction of dedicated stroke centers, which would reduce the barrier to seeking care in low-resource regions.[31] This effort should be supported by regional ministries of health to ensure access to care for all patients. One aspect of this care that could not be explored in this meta-analysis was how the time from injury to treatment influenced the use of TPA or MT. Further studies are required to investigate the outcomes of these therapies for AIS in different populations and across hospital systems in LMICs. A way to tackle disparities in treatment options between LMICs and HICs could be to introduce dedicated stroke centers in key areas of the countries or improve training opportunities for physicians in these areas. Additional studies should also be implemented to see how interventions, such as dedicated stroke centers, can impact the use of MT or TPA within these countries.

 

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Strengths and Limitations

As far as the authors are aware, this is the first meta-analysis exploring the impact of SDOH factors on accessibility of TPA and MT for AIS in LMICs. The comprehensive use of this meta-regression also allows us to investigate relationships between a variety of factors, including patient demographics and socioeconomic factors of the country. However, limitations in this study were the consequence of using only previously published data to explore the relationship between treatment use and SDOH factors. The rate of each therapy may be biased based on the objective of the study, and we rely on appropriate and consistent reporting across studies. The data included in the publication may also vary, especially regarding time to follow-up and time from symptom onset to treatment. Additionally, limited data were available on the specific socioeconomic factors within the region of the study since these were compared for the country overall. The represented cohorts were only from select countries that have published on this topic, and these studies only include stroke events that were able to make it to the hospital. Therefore, the findings that we report on may not apply to all countries or hospitals. In addition to the limited data available, the quality of a large proportion of the included articles was reported as poor, as assessed with the NOS, which is important to consider when evaluating these data. Emphasis is needed on the publication of high-quality, comprehensive studies exploring the relationship between SDOH, treatments, and outcomes for AIS in LMICs.

 

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Conclusion

This study uniquely looks at how SDOH impacts the use and accessibility of TPA and MT in LMICs. It is well established that these countries suffer from a higher disease burden for AIS, and they have fewer neurosurgically trained professionals per capita compared to HICs. We found that socioeconomic factors were associated with both treatments, TPA and MT, indicating more accessibility to these options in countries with better socioeconomic standings. Globally, SDOH impact the care and likely the outcomes following AIS. These results help support the need for improved infrastructure to reduce the disparity that is seen in AIS treatment in LMICs.

 

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Conflict of Interest

None declared.

• References

• 1 Feigin VL, Brainin M, Norrving B. et al. World Stroke Organization (WSO): Global Stroke Fact Sheet 2022. Int J Stroke 2022; 17 (01) 18-29
• 2 Feske SK. Ischemic stroke. Am J Med 2021; 134 (12) 1457-1464
• 3 Punchak M, Mukhopadhyay S, Sachdev S. et al. Neurosurgical care: Availability and access in low-income and middle-income countries. World Neurosurg 2018; 112: e240-e254
• 4 Reshetnyak E, Ntamatungiro M, Pinheiro LC. et al. Impact of multiple social determinants of health on incident stroke. Stroke 2020; 51 (08) 2445-2453
• 5 Skolarus LE, Sharrief A, Gardener H, Jenkins C, Boden-Albala B. Considerations in addressing social determinants of health to reduce racial/ethnic disparities in stroke outcomes in the United States. Stroke 2020; 51 (11) 3433-3439
• 6 Yadav RS, Chaudhary D, Avula V. et al. Social determinants of stroke hospitalization and mortality in United States' counties. J Clin Med 2022; 11 (14) 4101
• 7 Covell MM, Naik A, Shaffer A. et al. Social determinants of health impact spinal cord injury outcomes in low- and middle-income countries: A meta-epidemiological study. Neurosurgery 2024; 94 (05) 893-902
• 8 Sarraj A, Savitz S, Pujara D. et al. Endovascular thrombectomy for acute ischemic strokes: Current US access paradigms and optimization methodology. Stroke 2020; 51 (04) 1207-1217
• 9 Bambauer KZ, Johnston SC, Bambauer DE, Zivin JA. Reasons why few patients with acute stroke receive tissue plasminogen activator. Arch Neurol 2006; 63 (05) 661-664
• 10 Page MJ, McKenzie JE, Bossuyt PM. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; 372 (71) n71
• 11 Patel A, Vieira MMC, Abraham J. et al. Quality of the development of traumatic brain injury clinical practice guidelines: A systematic review. PLoS ONE 2016; 11 (09) e0161554
• 12 Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol 2010; 25 (09) 603-605
• 13 Hilbrich L, Truelsen T, Yusuf S. Stroke and cardiovascular diseases: the need for a global approach for prevention and drug development. Int J Stroke 2007; 2 (02) 104-108
• 14 Naik A, Bederson MM, Detchou D. et al. Traumatic brain injury mortality and correlates in low- and middle-income countries: A meta-epidemiological study. Neurosurgery 2023; 93 (04) 736-744
• 15 Niklasson A, Herlitz J, Jood K. Socioeconomic disparities in prehospital stroke care. Scand J Trauma Resusc Emerg Med 2019; 27 (01) 53
• 16 Abilleira S, Lucente G, Ribera A, Permanyer-Miralda G, Gallofré M. Patient-related features associated with a delay in seeking care after stroke. Eur J Neurol 2011; 18 (06) 850-856
• 17 Asif KS, Otite FO, Desai SM. et al.; Global Executive Committee of the MT-GLASS Study. Mechanical Thrombectomy Global Access For Stroke (MT-GLASS): A Mission Thrombectomy (MT-2020 Plus) Study. Circulation 2023; 147 (16) 1208-1220
• 18 Kleinig TJ, McMullan P, Cloud GC, Bladin PC, Ranta A. Hyper-acute stroke systems of care and workflow. Curr Neurol Neurosci Rep 2024; 24 (10) 495-505
• 19 Child N, Fink J, Jones S, Voges K, Vivian M, Barber PA. New Zealand National Acute Stroke Services Audit: Acute stroke care delivery in New Zealand. N Z Med J 2012; 125 (1358): 44-51
• 20 Rahbar MH, Medrano M, Diaz-Garelli F. et al. Younger age of stroke in low-middle income countries is related to healthcare access and quality. Ann Clin Transl Neurol 2022; 9 (03) 415-427
• 21 Virani SS, Alonso A, Benjamin EJ. et al.; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart Disease and Stroke Statistics-2020 Update: A report from the American Heart Association. Circulation 2020; 141 (09) e139-e596
• 22 Krishnamurthi RV, Feigin VL, Forouzanfar MH. et al.; Global Burden of Diseases, Injuries, Risk Factors Study 2010 (GBD 2010), GBD Stroke Experts Group. Global and regional burden of first-ever ischaemic and haemorrhagic stroke during 1990-2010: Findings from the Global Burden of Disease Study 2010. Lancet Glob Health 2013; 1 (05) e259-e281
• 23 Ramphul K, Ramphul Y, Sombans S. et al. Incidence and mortality rates of acute ischemic stroke in hospitalized patients in the United States. Arch Med Sci Atheroscler Dis 2021; 6: e132-e134
• 24 Kelly DM, Feld J, Rothwell PM, Reinecke H, Koeppe J. Admission rates, time trends, risk factors, and outcomes of ischemic and hemorrhagic stroke from German nationwide data. Neurology 2022; 99 (23) e2593-e2604
• 25 D'Anna L, Abu-Rumeileh S, Merlino G. et al. Safety and outcomes of mechanical thrombectomy in acute ischemic stroke attributable to cardiological diseases: A scoping review. J Am Heart Assoc 2024; 13 (17) e034783
• 26 Almallouhi E, Zandpazandi S, Anadani M. et al. Outcomes of mechanical thrombectomy in stroke patients with extreme large infarction core. J Neurointerv Surg 2024; 16 (12) 1268-1274
• 27 Romoli M, Princiotta Cariddi L, Longoni M. et al. Mechanical thrombectomy in ischemic stroke with a large infarct core: A meta-analysis of randomized controlled trials. J Clin Med 2024; 13 (15) 4280
• 28 Boulanger JM, Lindsay MP, Gubitz G. et al. Canadian Stroke Best Practice Recommendations for Acute Stroke Management: Prehospital, Emergency Department, and Acute Inpatient Stroke Care, 6th Edition, Update 2018. Int J Stroke 2018; 13 (09) 949-984
• 29 Knauss S, Stelzle D, Emmrich JV, Korsnes MS, Sejvar JJ, Winkler AS. An emphasis on neurology in low and middle-income countries. Lancet Neurol 2019; 18 (12) 1078-1079
• 30 Lauinger AR, Naik A, Bah MG. et al. Letter: Trends in in-hospital mortality and neurological deficit rates following ischemic stroke in low- and middle-income countries. Neurosurgery 2024; 94 (01) e9-e12
• 31 Prust ML, Forman R, Ovbiagele B. Addressing disparities in the global epidemiology of stroke. Nat Rev Neurol 2024; 20 (04) 207-221

Publication History

Article published online:
19 July 2025