Socioeconomic status and stroke incidence, prevalence, mortality, and worldwide burden: an ecological analysis from the Global Burden of Disease Study 2017

Among 240 causes of death, stroke is globally the second cause of death after ischaemic heart disease [1], and it is projected to remain so by 2030 [2]. This rank varies slightly across low-income countries (LICs), lower-middle-income countries (LMICs), upper-middle-income countries (UMICs), and high-income countries (HICs) as classified by the World Bank (Table 1) [3]. Further, stroke survivors may suffer from disabilities, requiring temporary or lifelong assistance, resulting in an enormous burden, both in human and economic costs. Evidence suggests that socioeconomic deprivation is not only associated with stroke and its risk factors, but also increases stroke severity [4] and mortality [5], and stroke incidence at younger ages [4].

Distribution of stroke risk factors in the context of socioeconomic status is changing worldwide (SES; Additional file 1: Text S1); understanding these trends is helpful in reducing the risk, through allocation of resources to those modifiable risk factors with the highest impact on stroke (Fig. 1). From 1990 to 2010, the age-standardised incidence of stroke decreased significantly by 12% in HICs, while in LMICs, it increased, although non-significantly, by about 12% [6]. However, mortality rates decreased significantly in both groups of countries (mean 37% in HICs vs. 20% in LMICs), of which 31% (with about 80% of it in LMICs) were in children and young adults (below 65 years) [6]. Further, the available evidence indicates that almost 90% of cardiovascular disease, including stroke and myocardial infarction, is caused by potentially modifiable risk factors [7]. We aimed to study the global and SES-specific stroke incidence, prevalence, burden, and mortality and to estimate the role of age, sex, and modifiable risk factors in stroke mortality.

We extracted data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, coordinated by the Institute for Health Metrics and Evaluation, as of 19 May 2019 [8]. We reported global and SES-specific rates of age-standardised and age-specific stroke incidence, prevalence, mortality, and disability-adjusted life years (DALYs) lost per 100,000 population with 95% uncertainty interval (UI) based on the 25th and 975th values of the ordered 1000 draws between 1990 and 2017. The method for estimating the parameters is explained elsewhere [9‐11]. In summary, the GBD enterprise originated from the 1990 World Bank study that was commissioned to measure the status of global health. It comprises information from multiple sources including multiple databases such as MEDLINE, EMBASE, LILACS, Scopus, PubMed and Science Direct, Global Health Database, WHO library and WHO regional databases, VR systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites (DSS). DisMod-MR 2.0 was used as a meta-regression tool to pool the case fatality data and generate location-year-age-sex-specific case fatality rate estimates.

SES was defined based on the gross national per capita income, as classified by the World Bank (i.e. low-income, lower-middle-income, upper-middle-income, or high-income) [3], and the Socio-Demographic Index (SDI; i.e. low-SDI, low-middle-SDI, middle-SDI, high-middle-SDI, or high-SDI) being developed in the GBD 2016. We also retrieved ranks of ten leading causes of death and stroke-related death attributable to common modifiable risk factors in those regions. We calculated rates and proportions of the abovementioned factors in both and each sexes of different socioeconomic regions from 1990 to 2017. We assessed proportions of the incidence, prevalence, burden, and mortality of stroke in different age groups. Unless explicitly mentioned otherwise, all rates were age-standardised using the GBD standard and reported per 100,000 population [12]. We used Numbers (version 3.6.2 for Mac OS X, Apple Inc., USA) for data compilation, preliminary analyses, and making tables. We used Prism (version 6.0 h for Mac OS X, GraphPad Software Inc.) for analysis and making the graphs.

Thirty-one LICs include Afghanistan, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Comoros, Democratic Republic of the Congo, Eritrea, Ethiopia, Guinea, Guinea-Bissau, Haiti, Liberia, Madagascar, Malawi, Mali, Mozambique, Nepal, Niger, North Korea, Rwanda, Senegal, Sierra Leone, Somalia, South Sudan, Tanzania, The Gambia, Togo, Uganda, and Zimbabwe; 52 LMICs include Angola, Armenia, Bangladesh, Bhutan, Bolivia, Cambodia, Cameroon, Cape Verde, Congo, Côte d’Ivoire, Djibouti, Egypt, El Salvador, Federated States of Micronesia, Georgia, Ghana, Guatemala, Honduras, India, Indonesia, Jordan, Kenya, Kiribati, Kyrgyzstan, Laos, Lesotho, Mauritania, Moldova, Mongolia, Morocco, Myanmar, Nicaragua, Nigeria, Pakistan, Palestine, Papua New Guinea, Philippines, Sao Tome and Principe, Solomon Islands, Sri Lanka, Sudan, Swaziland, Syria, Tajikistan, Timor-Leste, Tunisia, Ukraine, Uzbekistan, Vanuatu, Vietnam, Yemen, and Zambia; 54 UMICs include Albania, Algeria, American Samoa, Argentina, Azerbaijan, Belarus, Belize, Bosnia and Herzegovina, Botswana, Brazil, Bulgaria, China, Colombia, Costa Rica, Croatia, Cuba, Dominica, Dominican Republic, Ecuador, Equatorial Guinea, Fiji, Gabon, Grenada, Guyana, Iran, Iraq, Jamaica, Kazakhstan, Lebanon, Libya, Macedonia, Malaysia, Maldives, Marshall Islands, Mauritius, Mexico, Montenegro, Namibia, Panama, Paraguay, Peru, Romania, Russian Federation, Saint Lucia, Saint Vincent and the, Grenadines, Samoa, Serbia, South Africa, Suriname, Thailand, Tonga, Turkey, Turkmenistan, and Venezuela; and 58 HICs include Andorra, Antigua and Barbuda, Australia, Austria, Bahrain, Barbados, Belgium, Bermuda, Brunei, Canada, Chile, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Greenland, Guam, Hungary, Iceland, Ireland, Israel, Italy, Japan, Kuwait, Latvia, Lithuania, Luxembourg, Malta, Netherlands, New Zealand, Northern Mariana Islands, Norway, Oman, Poland, Portugal, Puerto Rico, Qatar, Saudi Arabia, Seychelles, Singapore, Slovakia, Slovenia, South Korea, Spain, Sweden, Switzerland, Taiwan, The Bahamas, Trinidad and Tobago, United Arab Emirates, the UK, Uruguay, Virgin Islands, and the USA.

The results of the current study add to the body of evidence on existing disparities, gaps, and hurdles in stroke research, practice, and educational endeavours in different socioeconomic classes, countries, and regions. It appears that the age-standardised rate of stroke incidence and mortality is decreasing in all regions of varying SES (based on income-level or SDI), although more rapidly in wealthier societies. This influence is largely driven through commonly known modifiable stroke risk factors. In 2017, high systolic blood pressure and dietary risks were the top leading causes of stroke-related deaths and worldwide burden. Among the 11 modifiable risk factors, alcohol had five times higher association with haemorrhagic strokes than ischaemic strokes. Further, the rates of stroke mortality and burden were significantly higher in lower SES regions compared with HICs. Likewise, the age-standardised stroke mortality attributable to potentially modifiable risk factors is declining in almost all regions, except for obesity and alcohol use. LICs hold the worst attributable risk of stroke mortality for hypertension, dietary habits, diabetes, and air pollution, and UMICs hold the worst attributable risk of stroke mortality for obesity, tobacco use, alcohol use, hypercholesterolemia, and low physical activity. Still, HICs have almost threefold lower rates of stroke mortality attributable to modifiable risk factors compared to all other income categories.

According to the last report of the global, regional, and national burden of neurological disorders, there has been a significant reduction in the age-standardised prevalence of stroke (10% decrease) and death (30% decrease) from 1990 to 2015 [1]. By 2017, according to the present study, the age-standardised rates of global stroke prevalence and mortality exhibited a 3% increase and a 33% decrease, respectively. This reflects the evolving nature of the GBD data and the changes in stroke burden worldwide.

Based on the results of a meta-analysis of 12 population-based cohorts and case-control studies mainly in HICs, despite some limitations, reduced SES could explain more than 30% (95% confidence interval 16–48) of the stroke risk irrespective of classical vascular risk factors [21]. Based on another meta-analysis by Kerr et al. [21], blood pressure, smoking, diabetes, lipids, atrial fibrillation, history of vascular disease, obesity, and physical activity were overall associated with an additional 30–40% risk. Nevertheless, more population-based studies have been conducted since then, which improved our understanding of the predicting factors [22]. For instance, an Australian population-based study of 3077 subjects with incident stroke between 1995 and 2003 proposed that improving the SES of the most deprived inhabitants could prevent up to one fifth of strokes [23].

The disparity between stroke outcomes in LMICs and HICs can be partly explained by a combination of varying levels of general health awareness, access to healthcare, and preventative strategies starting from childhood [24, 25]. Stroke management is also challenging. Excessively increasing trends of urbanisation, pollution, smoking, obesity, less physical activity, having unhealthy diets, and ageing, particularly in LMICs, along with a much larger population with restricted access to health care may partially predict the increasing rates of stroke in these countries. This alarming increase necessitates a thorough investigation of all possible reasons, including SES determinants. Accordingly, despite many recent studies in LICs and LMICs [26‐36] (Additional file 5: Table S4), we cannot confidently generalise the association of common vascular risk factors with the risk and outcome of stroke in LICs and LMICs. There used to be a ten-fold difference in rates of stroke mortality and burden between the most-affected and the least-affected countries [37]. Currently, based on our findings, this has decreased to almost threefold, which is suggestive of a substantial improvement in healthcare, preventive measures, and therapeutic outcomes. However, we should acknowledge the scantiness of population-based studies in LICs and LMICs compared to UMICs and HICs.

According to the GBD 2015 [38], there are regular shifts in stroke-related cause of death composition and population age structure with rising SDI (Table 4). Moreover, ischaemic heart disease, stroke, and diabetes are among the leading causes of premature mortality, measured as years of life lost, in most SES regions based on SDI [38]. The present study highlighted the necessity of country-specific and SES-stratified quantification of the risk of stroke attributable to each factor [22].

Almost half of stroke-related mortality may be attributable to modifiable risk factors (i.e. hypertension, diabetes, dietary risks, impaired glucose intolerance, obesity, smoking, air pollution, alcohol use, hypercholesterolemia, and physical inactivity), which are mostly the outcome of poor clinical management, limited access to health care, and late detection of underlying risk factors. This necessitates allocation of resources to those modifiable risk factors with the highest impact on stroke in each SES-region. Moreover, social and economic policies to reduce inequalities in stroke care should become a health priority, particularly in less wealthy countries. These policies should focus on treating early predisposing factors and on educational programmes from childhood, which have long-lasting impacts on adulthood health. Likewise, improving worldwide primary healthcare services may have an important impact on post-stroke outcomes. It is essential to improve stroke awareness among socioeconomically deprived individuals and societies and provide equitable post-stroke medical care.