Socio-Economic Inequalities in Stroke: A Global Perspective

Olaleye Akinmola Adeniji; Ayotomiwa Fagbemi

doi:10.5772/intechopen.1010685

Abstract

The effects of socio-economic inequalities on stroke risk, clinical profile, and outcomes continue to attract global public health interest. This is because, despite impressive advances in stroke prevention and acute care, substantial heterogeneities remain with regard to stroke incidence, prevalence, prevention, treatment and outcomes across many parts of the world. Socio-economic factors may influence differences in access to resources, lifestyle and behavioral choices, psychosocial stressors and the built environment and may partly explain why these heterogeneities persist. The political and cultural environment in which people live, grow and work is also critical in shaping policy priorities and subsequent allocation of resources to non-communicable diseases such as stroke. In this chapter, an overview of the effects of socio-economic inequalities on stroke risk factors, incidence, prevalence, mortality, is provided. Potential strategies for advancing equity in stroke care, especially across diverse populations, are also discussed.

Keywords

socio-economic inequalities
stroke
low socio-economic status
disparities
social determinants

Author Information

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Olaleye Akinmola Adeniji *
- Department of Neurology, University College Hospital, Ibadan, Oyo State, Nigeria
Ayotomiwa Fagbemi
- Neurology Unit, Federal Medical Center, Abeokuta, Ogun State, Nigeria

*Address all correspondence to: leyeadeniji@yahoo.co.uk

1. Introduction

Stroke is a major disabling disease of global public health importance. It is the second leading cause of death and third leading cause of adult-onset disability globally [1]. Stroke outcomes such as death, disability, dementia, depression and anxiety are major contributors to increased costs of care, caregiver burden and poor quality of life among stroke survivors and their families [2, 3]. Over the years, understanding of stroke risk factors, clinical presentation, outcomes and global burden has increased in tandem with impressive advances in stroke prevention, care and treatment. However, despite these, substantial heterogeneity exists in the global stroke burden [1]. This may be partly because of socio-economic factors prevalent within the population and among individuals that influence stroke epidemiology and outcomes [4]. These factors sometimes also called social determinants of health (SDOH), have become of increasing interest in global public health [4]. The World Health Organization (WHO) Commission on the SDOH in 2010 defined SDOH as non-medical factors that influence health outcomes [5]. These include structural factors such as the political, legal, economic, educational and cultural policies or beliefs prevalent within societies that determine the gradients of income, education, occupation or social class [6]. These structural factors play an important role in shaping the material, biological and psychosocial living situation of the individual or society including factors such as housing, food availability, early childhood factors, the lived environment, physical activity and mental well-being. These “downstream” factors are referred to as intermediate determinants of health [6]. The accretionary effects of these structural and intermediary determinants play a critical role in shaping differences in stroke risk within populations (inequalities) and eventual outcomes after stroke (disparities) [7] (Figure 1) . A better understanding of the drivers of these determinants has, however, emerged, highlighting a strong association between low socio-economic status (a composite metric including many indices such as level of education, neighborhood socio-economic status and income level) and stroke risk and outcomes [8, 9, 10] and are reviewed in this chapter. Based on these, some strategies for addressing these inequalities in population stroke prevention and care have been suggested and are also reviewed.

Figure 1.
A diagram showing the interaction between structural (red squares) and intermediate determinants of health (text) and stroke risk, stroke care and outcomes (Venn diagram).

1.1 Stroke risk factors

Low socio-economic status (SES) is an important driver of inequalities in stroke risk from many studies [11, 12, 13, 14]. The overall effects of modifiable stroke risk factors such as hypertension, diabetes mellitus, obesity, sedentary lifestyle, and alcohol and tobacco use have a strong correlation with low SES in both high-income countries (HICs) [11] and low- and middle-income countries (LMICs) [4]. The association between low SES and dyslipidemia has also been reported [15]. High SES has also been documented as a risk factor for stroke [16], although overall low SES seems to be associated with more severe strokes than high SES [4]. This may be because higher SES increases the adoption of urbanization, lifestyles and diets that increase the risk of stroke.

Although the definitions of low SES vary across different studies [11], a consistent pattern of higher stroke risk with increasing indices of low SES has been reported [11, 13, 14]. Several other indices of low SES such as unemployment status [12], rural habitation [17], non-serviced accommodation [17], low educational level [18] and lack of health insurance [19] have also been associated with higher risks of stroke. These associations seem to be present within both HICs and LMICs [11, 18, 19], suggesting that stroke risk factors may be driven by similar socio-economic variables across diverse populations. Data from the Global Burden of Disease Investigators (2019) also suggest that low SES is strongly associated with the risk of stroke and stroke burden [1].

The effects of SES on stroke risk may even be more pronounced in certain ethnic groups. In the United States, for example, people of African Ancestry have a higher incidence of stroke, more severe strokes and higher stroke mortality than other racial groups and this risk has persisted despite an overall reduction in stroke incidence in the general population over the last five decades [20, 21]. Markers of low SES such as low income have been reported as associated with a higher risk of stroke among African Americans compared to other ethnic groups although other factors such as genetic and epigenetic factors have also been suggested [21]. In the REGARDS trial, for example, each additional SDOH factor was associated with higher odds of stroke, especially in individuals under 75 years of age [22].

The pathways by which low SES increases stroke risk factors such as hypertension, diabetes, obesity, poor nutrition, sedentary lifestyle, alcohol and tobacco consumption among low SES populations are likely complex and multifaceted. Stroke knowledge and awareness has been reported to be poorer among people with low SES [23], and this may lead to poor health-seeking behavior. The burden of infectious diseases such as HIV has also been reported to be higher among people with low SES, and this may impact stroke risk, especially in regions of high HIV endemicity [24]. Further, structural and intermediate barriers to equitable access to health appear more prevalent among people with low SES. People with low SES, especially in LMICs, have higher out-of-pocket expenditures and higher rates of catastrophic health expenditure (a marker of impoverization due to illness) due to stroke [25]. These barriers may prevent early presentation and treatment of stroke risk factors and poorer outcomes.

1.2 Early life factors

Markers of low SES in early life, such as inadequate nutrition, lower parental social class and poor social mobility, have also been associated with increased stroke risk [26]. These markers may influence stroke risk across the life course. The fetal origins hypothesis by Barker [27], which highlights how adverse early life factors such as poor nutrition increase the risk of cardiovascular diseases such as stroke in later life, is relevant in linking adverse early life factors to increased stroke risk in later life. Studies suggest that individuals with anthropomorphic indices of inadequate nutrition such as low birth weight, pre-term birth and short stature have an increased risk of stroke risk factors such as hypertension, diabetes and obesity in later life [26, 28]. Paternal social class and a positive family history of stroke death have also been associated with increased stroke risk [29, 30]. Inter-generational social mobility has also been associated with increased risk of ischemic stroke with downward social mobility especially between adolescence and early adulthood being associated with higher odds of stroke in later life [31].

1.3 Stroke incidence

According to the GBD investigators 2021 [1], stroke incidence seems to be increasing globally. Stroke incidence increased, especially in parts of Southeast Asia and Oceania [1], from 1990 to 2020. This contrasted with the picture in 2017, when there was an overall 11.3% decline in age-standardized stroke incidence globally [4]. This observed increase in stroke incidence was seen mostly in young to middle-aged people. Notably, from the 2021 study, there was a strong association between increasing stroke incidence and lower socio-demographic indices (SDIs) [1]. The major drivers of increasing stroke incidence include demographic factors such as increasing aging of the population, stroke risk factors such as uncontrolled hypertension, poor lifestyle choices (obesity, smoking, alcohol use and sedentary lifestyle) and air pollution [1]. These factors act in synergy with other system factors such as absence or poorly developed stroke prevention and treatment services, inadequate stroke care manpower and low levels of public spending on healthcare.

Generally, stroke incidence appears to be higher among people with markers of low SES such as low level of education, low-income status and low neighborhood social class [11, 14]. This is likely due to the higher burden of stroke risk factors and poor screening and treatment among people with low SES.

1.4 Stroke prevalence

Age-standardized stroke prevalence seems to have doubled over the past 30 years despite advances in prevention and treatment of stroke [1, 4]. This trend of increasing stroke prevalence seems to be predominant in LMICs [4]. The relationship between low SES and stroke prevalence in LMICs also seems to be non-linear, however, with increasing stroke prevalence at both high and lower ends of the socio-economic strata [32, 33]. People with low SES in urban areas appear to have a higher prevalence of stroke compared to those in rural areas [33].

1.5 Stroke mortality

It is estimated that global stroke mortality will increase by 50% from 2020 to 2050, and a substantial part of this mortality burden would be borne by LMICs [34]. Already, LMICs bear a disproportionate burden of stroke mortality while having the least number of human resources to tackle it [1, 34]. According to the Global Burden of Diseases Injuries and Risk Investigators 2021 [1], 87.2% of all fatal strokes, and 89.4% of all stroke-related Disability-Adjusted Life-Years (DALYs) occurred in all low-income and middle-income countries (LMICs) combined in 2021. The drivers of increasing stroke mortality include absent or poorly developed acute stroke treatment services, poorly developed pre-hospital stroke services and huge financial barriers to accessing acute stroke care due to absence of universal health coverage [34]. Although data on specific racial, gender and socio-economic disparities in stroke mortality in LMICs is still emerging, their overall effects are likely to be significant due to challenges with resources available for healthcare and non-communicable disease prevention. In contrast, HICs have experienced a progressive decline in stroke mortality [35, 36]. This decline in stroke mortality in HICs occurred despite increasing aging in many of these countries [37, 38]. The main drivers of reducing stroke mortality in HICs likely include relatively reduced stroke incidence (due to systematic improvements in population health and population stroke risk factor control) and reduced stroke case-fatality rates (due to advances in acute stroke treatments such as thrombolysis and mechanical thrombectomy for ischemic strokes, decompression and evacuation surgeries for hemorrhagic strokes and the uptake of acute stroke units) [35, 36, 37, 38]. In many HICs, these declines in stroke mortality have been accompanied by concomitant declines in mortality from ischemic heart disease and dementia, suggesting a pragmatic model for the control of not just stroke but other NCDs as well [39].

2. Equity-based approaches to reducing socio-economic inequalities

Health equity is defined as the reduction or elimination of all remediable or unfair differences in health and health outcomes among different social, ethnic, gender, religious and geographic population groups [40] and has become a topical issue in public health. The concept is relevant to stroke prevention and care because of the effect of socio-economic inequalities on stroke risk and outcomes. Despite the advances in stroke prevention and acute stroke care over the past decades, the promise of access to safe, timely, effective, affordable and high-quality stroke care remains challenging for diverse populations due to systemic and structural barriers from differences in race or ethnicity, gender, economic class or educational level. A range of solutions has been proposed and operationalized within different models of stroke care with a view to reducing some of the observed disparities in stroke. Many of these solutions have their strengths and weaknesses, but they provide useful templates for advancing equity in stroke care and are discussed below.

2.1 Stroke surveillance and prevention

Stroke surveillance refers to systematic attempts by communities or countries to map the risk factors for stroke within populations including regular surveys of populations at risk with a view to early screening and treatment of these stroke risk factors [41]. It is an important component of the stroke quadrangle [41] (comprising stroke surveillance, prevention, acute treatment and rehabilitation). These attempts may involve mass surveys of populations at risk with a focus on identifying stroke risk factors and determining individual risks. Stroke prevention also involves strategies to modify the population determinants of stroke risk factors and to prevent the establishment of these risk factors (primordial prevention) or strategies to actively treat those populations who already have stroke risk factors to prevent the occurrence of a stroke (primary prevention) [42]. These (primordial and primary prevention) may involve public legislation and educational programs, bans and taxes on tobacco and alcohol products, physical activity campaigns and active screening and treatment of stroke risk factors by trained staff usually at the primary level of care (population-based strategy) [42]. A second strategy involves identification of individuals or population subgroups particularly at high risk of stroke (high-risk strategy) [43]. While population-based approaches are thought to be more cost effective, questions remain about feasibility and sustainability [43]. High-risk strategy of screening and treatment relies mostly on individual risk-stratification and treatment of high-risk individuals [43].

Digital tools for stroke risk stratification have also been developed and used [44]. One of the more commonly known and widely recommended tools is the Stroke Risko-meter Application [45]. However, many of the currently available risk-stratification models, including the Stroke Risko-meter Application, do not include markers of Socio-economic status or SDOH. Further, these applications were derived from specific populations and may therefore underestimate or overestimate stroke risk in non-derived populations [44, 45]. Tele-health tools such as the Stroke Coach have also been studied as a pragmatic means of screening and identifying at-risk populations, disseminating stroke knowledge and promoting healthy lifestyle and behavioral choices such as smoking cessation and tobacco use cessation [46]. There is evidence that these tele-health tools may be effective at improving glycemic control, dyslipidemia and weight loss [47]. However, their overall efficacy in reducing stroke risk is still being investigated.

Challenges facing digital approaches to stroke primary prevention include the digital health divide between HICs and LMICs, structural barriers such as availability of telephony infrastructure in rural areas, broadband challenges and costs [44, 45]. Many of these digital tools require some level of literacy or numeracy which may also be a challenge in low-literacy populations.

2.1.1 Task shifting and sharing

Task shifting is defined as the systematic transfer of primary care duties from physicians to non-physician healthcare workers, such as nurses, pharmacists or community health workers [48]. Task shifting may be a pragmatic approach where there are no physicians. However, in areas with low physician densities, task sharing involving appropriately trained and certified non-physician health workers may be useful in assisting the physician to deliver healthcare services [48]. Task-sharing strategies have been reported to be feasible and effective in improving the screening and management of stroke risk factors in community settings [49]. Data from many studies and meta-analyses suggest that task sharing with non-physician health workers is effective for the management of blood pressure and diabetes mellitus in both community and hospital-based settings in both HICs and LMICs [50, 51, 52]. However, many gaps remain in effectively deploying this strategy at scale [51]. The effectiveness of a task-sharing strategy in improving overall cardiovascular risks is also under investigation.

3. Future directions and conclusion

As research on the socio-economic inequalities in stroke care and outcomes continues to grow, priorities for improving these inequalities will become of increasing political and economic interest, in tandem with the projected increase in global stroke burden. Therefore, accurate characterization of the impact of different socio-economic determinants of health within diverse populations will be key for research, clinical practice and policy implementation. Delineating the effect sizes of various SDOH using highly rigorous methods will also be important to provide insights into the best areas to concentrate public health resources. Implementation strategies to improve these SDOH will also be critical. Endpoints such as feasibility, sustainability and cost-effectiveness of these interventions will be important indices. Overall, political will and multi-sectoral strategies will be important determinants of respective countries outcomes in the efforts to improve these markers of socio-economic inequalities and optimize stroke care and outcomes for vulnerable populations.

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[1] 1. Feigin VL, Abate MD, Abate YH, Abd ElHafeez S, Abd-Allah F, Abdelalim A, et al. Global, regional, and national burden of stroke and its risk factors, 1990-2021: A systematic analysis for the Global Burden of Disease Study 2021. The Lancet Neurology. 2024;23(10):973-1003

[2] 2. Gorelick PB. The global burden of stroke: Persistent and disabling. The Lancet Neurology. 2019;18(5):417-418

[3] 3. Lanas F, Seron P. Facing the stroke burden worldwide. The Lancet Global Health. 2021;9(3):e235-e236

[4] 4. Avan A, Digaleh H, Di Napoli M, Stranges S, Behrouz R, Shojaeianbabaei G, et al. Socioeconomic status and stroke incidence, prevalence, mortality, and worldwide burden: An ecological analysis from the Global Burden of Disease Study 2017. BMC Medicine. 2019;17(1):1-30

[5] 5. Solar O, Irwin A. A conceptual framework for action on the social determinants of health. (Social determinants of health discussion paper 2 (policy and practice)). Geneva, Switzerland: WHO; 2010. [September 22, 2016]. Available from: http://www.who.int/sdhconference/resources/ConceptualframeworkforactiononSDH_eng.pdf [Accessed: 19th May 2025]

[6] 6. Williams GH. The determinants of health: Structure, context and agency. Sociology of Health & Illness. 2003;25(3):131-154

[7] 7. Ovbiagele B. Decoding stroke disparities: Zip codes, color codes, and epigenetic codes. Transactions of the American Clinical and Climatological Association. 2024;134:214

[8] 8. Pantoja-Ruiz C, Akinyemi R, Lucumi-Cuesta DI, Youkee D, Emmett E, Soley-Bori M, et al. Socioeconomic status and stroke: A review of the latest evidence on inequalities and their drivers. Stroke. 2025;56:794-805

[9] 9. Sommer I, Griebler U, Mahlknecht P, Thaler K, Bouskill K, Gartlehner G, et al. Socioeconomic inequalities in non-communicable diseases and their risk factors: An overview of systematic reviews. BMC Public Health. 2015;15:1-2

[10] 10. Cox AM, McKevitt C, Rudd AG, Wolfe CD. Socioeconomic status and stroke. The Lancet Neurology. 2006;5(2):181-188

[11] 11. Marshall IJ, Wang Y, Crichton S, McKevitt C, Rudd AG, Wolfe CD. The effects of socioeconomic status on stroke risk and outcomes. The Lancet Neurology. 2015;14(12):1206-1218

[12] 12. Avendano M, Kawachi I, Van Lenthe F, Boshuizen HC, Mackenbach JP, Van den Bos GA, et al. Socioeconomic status and stroke incidence in the US elderly: The role of risk factors in the EPESE study. Stroke. 2006;37(6):1368-1373

[13] 13. Kumar A, Prasad M, Kathuria P, Nair P, Pandit AK, Sahu JK, et al. Low socioeconomic status is an independent risk factor for ischemic stroke: A case-control study in North Indian population. Neuroepidemiology. 2015;44(3):138-143

[14] 14. Amarenco P, Abboud H, Labreuche J, Arauz A, Bryer A, Lavados PM, et al. Impact of living and socioeconomic characteristics on cardiovascular risk in ischemic stroke patients. International Journal of Stroke. 2014;9(8):1065-1072

[15] 15. Pirillo A, Casula M, Olmastroni E, Norata GD, Catapano AL. Global epidemiology of dyslipidaemias. Nature Reviews Cardiology. 2021;18(10):689-700

[16] 16. Yamanie N, Sjaaf AC, Felistia Y, Susanto NH, Diana A, Lamuri A, et al. High socioeconomic status is associated with stroke severity among stroke patients in the National Brain Centre Hospital, Jakarta, Indonesia. Preventive Medicine Reports. 2023;32:102170

[17] 17. Singh GK, Siahpush M, Azuine RE, Williams SD. Increasing area deprivation and socioeconomic inequalities in heart disease, stroke, and cardiovascular disease mortality among working age populations, United States, 1969-2011. International Journal of MCH and AIDS. 2015;3(2):119

[18] 18. McHutchison CA, Backhouse EV, Cvoro V, Shenkin SD, Wardlaw JM. Education, socioeconomic status, and intelligence in childhood and stroke risk in later life: A meta-analysis. Epidemiology. 2017;28(4):608-618

[19] 19. Shen JJ, Washington EL. Disparities in outcomes among patients with stroke associated with insurance status. Stroke. 2007;38(3):1010-1016

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