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Title
Ischemic and hemorrhagic stroke in Brazil: trends in mortality and correlation with socioeconomic indicators over 20 years

Abstract: This study investigated trends in stroke mortality in Brazil at national, regional and state level and their correlation with the Municipal Human Development Index (MHDI) and Social Vulnerability Index (SVI). We conducted an ecological time series study of deaths during the period 2000-2019. International Classification of Diseases (10th Edition) codes for causes of death from stroke were divided into three groups: ischemic stroke (IS), hemorrhagic stroke (HS) and unspecified stroke (US). Temporal trend analysis was performed using joinpoint regression. The Southeast accounted for the highest number of deaths. The results of the stratified analysis reveal a general downward trend in HS and US mortality in Brazil, with an increase in IS and HS mortality in the North and Northeast. There was a strong positive correlation between average annual percent change (AAPC) for the MHDI and trends in HS mortality rates among the 40-59 year age group and above and trends in IS mortality in the 80 years and over group. There was no correlation between AAPC for the SVI and trends in mortality across all causes of death from stroke. It is concluded that the upward trend in IS and HS in the North and Northeast may be linked to social and economic inequality in these regions. The downward trend in US mortality seems to be influenced by increased IS mortality, which may be explained by improvements in mortality information system data quality.
Keywords: Cerebrovascular Disease, Mortality, Development Indicators.



Introduction
Cerebrovascular disease is the second leading cause of death worldwide, accounting for 10.2% of all deaths in 2016, with 4.9% of deaths being attributed to ischemic stroke (IS) and 5.2% to hemorrhagic stroke (HS)1.
In 2019, more than 6 million people died from stroke worldwide and it is estimated that this number could reach 10 million a year by 20602. In middle-income countries such as Brazil it is estimated that the percentage of deaths from stroke will decrease from 15.2%, in 2016, to 12.8% in 2060, while in low-income countries forecasts suggest an increase over the same period from 5.2% to 10.0%3.
The stroke case fatality rate is around 15% within 1 month, 25% within 1 year and 50% within 5 years, and approximately 40% of stroke survivors become disabled between 1 month and 5 years after stroke4. Case fatality rates for HS are around 55% within 1 year and 70% within 5 years5.
Prevalence of stroke in Brazil varies from study to study and across states, from 1.3% to 6.8%6–8. This wide variation has been explained by the continental proportions of the country, socioeconomic inequality9 and differences in data analysis methods between studies.
Death certificates registered in the country’s mortality information system (SIM, acronym in Portuguese) provide a considerable amount of information for analysis and disease surveillance, enabling monitoring of deaths by disease and the calculation of projections for Brazil’s health system10.
Most studies in the literature on stroke include all the codes of the Tenth Revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) inherent to the disease. Strokes that may cause brain damage result in increased morbidity and are therefore more likely to be involved in the underlying cause of death. However, some studies fail to separate IS and HS, which both affect different age groups differently and have different risk factors, thus hampering the interpretation of results11.
The Human Development Index (HDI) was introduced in 1990, when the United Nations Development Programme (UNDP) launched the first Human Development Report. In 2012, UNDP Brasil, together with the Institute of Applied Economic Research (IPEA) and the João Pinheiro Foundation, adapted the HDI method to create the municipal HDI (MHDI), used to assess the country’s 5,565 municipalities. The MHDI was calculated using data from the last three demographic censuses conducted by the Brazilian Institute of Geography and Statistics (IBGE) – 1991, 2000 and 2010 – and based on the municipal grid in 201012. The Social Vulnerability Index (SVI) creates a synthetic indicator of the absence or insufficiency of essential resources needed to ensure well-being and quality of life. The method used resembles that of the MHDI. The data available from 2017 include the same indicators used between 2000 and 2010.
The calculation of indicators, dimensions and indices is based on crude data produced by the National Household Sample Survey (PNAD) between 2011 and 201513.
These two indicators enable the monitoring of human development and trends in social vulnerability at municipal level, providing important inputs to shape public policies and evaluate their effectiveness.
Studies investigating the association between socioeconomic indicators and risk of stroke among adults have produced conflicting results, with both positive and negative correlations13,14. In Brazil, these indicators show the same discrepancies at national level and across regions15.
The objective of this study was therefore to analyze trends in deaths from IS and HS in Brazil at national, regional and state level and their correlation with the MHDI and SVI during the period 2000-2019.
Method
Study design, population and period
We conducted an ecological time series study of deaths during the period 2000-2019 where the underlying cause was stroke.
Data collection and sources
We collected data on causes of death due to stroke based on the ICD-10 codes from individual databases in the SIM and on other conditions and factors recorded on death certificates. For the purposes of this study, we included strokes that may cause brain damage which, in principle, are detectable by neuroimaging. The ICD-10 codes for these diseases include unspecified causes such as hemorrhage or ischemia. The causes were therefore categorized into three groups: a) ischemic stroke (IS) - I63 (cerebral infarction), I67.3 (progressive vascular leukoencephalopathy), I67.8 (other specified cerebrovascular diseases - acute cerebrovascular insufficiency and cerebral ischemia) and I69.3 (sequelae of cerebral infarction); b) hemorrhagic stroke (HS) - I60 (subarachnoid hemorrhage), I61(intracranial hemorrhage), I62 (other nontraumatic intracranial hemorrhage), I69.0 (sequelae of subarachnoid hemorrhage), I69.1 (sequelae of intracranial hemorrhage) and I69.2 (sequelae of other nontraumatic intracranial hemorrhage); and c) unspecified hemorrhagic ischemic or hemorrhagic stroke (US) - I64 (stroke not specified as hemorrhagic or ischemic), I67.9 (unspecified stroke), I69.4 (sequelae of stroke not specified as hemorrhagic or ischemic) and I69.8 (sequelae of other cerebrovascular diseases and unspecified sequelae).
While the database of the national health system’s Department of Informatics (DATASUS) considers stroke to be codes I60-I69, the pathophysiology and etiology of some of these causes are very particular and, in other cases, detectable brain damage does not occur. For this reason, the following codes were excluded: I65 (occlusion and stenosis of precerebral arteries not resulting in cerebral infarction), I66 (occlusion and stenosis of cerebral arteries not resulting in cerebral infarction), I67.0 (dissection of cerebral arteries, nonruptured), I67.1 (cerebral aneurysm, nonruptured), I67.2 (cerebral atherosclerosis), I67.4 (hypertensive encephalopathy), I67.5 (moyamoya disease), I67.6 (nonpyogenic thrombosis of intracranial venous system without cerebral infarction), and I67.7 (cerebral arteritis, not elsewhere classified). Code I68 (cerebrovascular disorders in diseases classified elsewhere) was also excluded because it refers to diseases with defined etiology without necessary identifiable brain damage.
The study population consisted of adults divided into the following age groups: 20-39 years; 40-59 years; 60-79 years; 80 years and over; and overall (all age groups).
Two social and economic development indicators were selected for correlation: i) the MHDI, which measures the same key dimensions of human development as the HDI: life expectancy, education and income. The MHDI tailors the HDI methodology to the Brazilian context and the availability of national indicators. The closer to 1 the better the MHDI; ii) the SVI consists of three dimensions - urban infrastructure, human capital, and income and employment – representing three sets of assets that determine well-being in contemporary societies. In contrast to the MHDI, the closer to 1 the worse the indicator.
The HDI data were obtained from the 2010 Atlas of Human Development12 and the SVI data were taken from the 2010 Atlas of Social Vulnerability16. The MHDI and SVI were calculated for the census years (2000 and 2010) and based on crude PNAD data (2011-2015).
Statistical analysis
We calculated standardized annual crude rates of mortality per 100,000 population17 using the direct method, based on the overall population during the period 2000-2019, regardless of sex. The population data was extracted from the DATASUS website18. National and regional mortality rates were standardized by age group and sex for each group of causes. The analyses were performed using R.
Temporal trend analysis was performed using the joinpoint regression model to identify statistically significant points of inflection and annual percent change in mortality rates between 2000 and 2019. This method allows the researcher to detect trends (stationary, upward or downward) in each indicator, joinpoints, and annual percent change (APC) and average annual percent change (AAPC). The number of joinpoints was calculated using the permutation test with Bonferroni correction, adopting a 95% confidence interval (95% CI) and 5% significance level. These analyses were conducted using Joinpoint Regression 4.5.0.1 (National Cancer Institute, USA)19.
We calculated AAPC for the socioeconomic indicators and respective dimensions over the study period in each state. The correlation between the AAPC of the mortality rates was assessed across the groups of causes of death from stroke and AAPC for the socioeconomic indicators and their respective dimensions by age group.

Results
There were 1,924,715 deaths from the underlying causes studied, distributed as follows: IS – 361,072 (19%); HS – 442,434 (22%); US – 1,121,209 (59%). The Southest accounted for the largest share of overall deaths (44.13%), followed by the Northeast (26.10%), South (18.83%), Midwest (5.76%) and North (5.18%) (Table 1).
Women accounted for 50.29% of deaths. The 60-79 age group accounted for the largest proportion of deaths (44.92%), followed by the 80 years and over group (35.59%), 40-59 age group (16.93%) and 20-39 group (2.56%). When the data were stratified by sex, the only age group in which the proportion of women was higher than the proportion of men was the 80 years and over group. Mortality was higher in the Southeast, South and Midwest (Table 1).
The results of the stratified analysis of deaths by group of causes and region over the whole study period (AAPC) revealed a general downward national trend in HS and US and a stationary trend for IS. However, when the data were stratified by period (APC), there was an increase in mortality from 2015. This increase was more pronounced in the two oldest age groups. An upward trend in IS was observed across all regions except the Northeast. US showed a significant upward trend in the North and Northeast from 2000 to 2007 in the 80 and over age group, while HS increased between 2000 and 2017 in the North and over the whole period in the Northeast in this age group (Table 2).
The largest reductions in IS and HS mortality were found in the below 60-year age group. In contrast, the oldest age group showed an increase in mortality rates in the North and Northeast, a stationary trend in the Midwest and downward trend in the South and Southeast. The US mortality rate showed a downward trend across all age groups in the Midwest, Southeast and South. In contrast, in the 80 and over age group there was a substantial increase in rates in the states of Amazonas, Maranhão, Piauí, Paraíba, Alagoas and Sergipe (Figure 1).
The results of the analysis of the socioeconomic development indicators showed that AAPC for MHDI represented an increase across all states. The largest increase occurred in the North and Northeast. The dimension with the largest AAPC was education. The state with the highest MHDI in 2019 was the Federal District (0.86) (Table 3).
AAPC for SVI represented a decrease across all regions. The largest negative AAPC was found in Rondônia (-4.59%), followed by Mato Grosso do Sul (-4.35%) and Santa Catarina (-3.99%). The lowest AAPC in 2019 were found in states in the Southeast, South and Midwest. The SVI dimension with the largest negative AAPC was urban infrastructure, with values of -12.25% in Amapá and -9.1% in Rondônia (Table 3).
The findings show a strong positive correlation between trends in MHDI over the study period (AAPC) and trends in HS mortality in the 40–59-year age group and above and trends in IS mortality in the 80 years and over age group. All the dimensions of the MHDI showed a similar positive correlation, except for income, which did not show any correlation with IS. US mortality showed a strong positive correlation with MHDI across all its dimensions (Table 4).
There was no correlation between trends in SVI (AAPC) and trends in mortality rates across all groups of disease and age groups. Infrastructure was the only SVI dimension to show a negative (moderately significant) correlation with HS and US mortality rates in the three oldest age groups. In contrast, income showed weak and moderately significant positive correlations with US and capital showed moderately significant positive correlations with HS and US in the 40-59 year age group and above (Table 4).
Discussion
In general, the findings show a stationary trend for IS and a reduction in HS mortality at national level over the study period. This is consistent with World Health Organization (WHO) projections3.
Brazil’s first stroke unit was created in Joinville, Santa Catarina in 1997. In 2008, the Ministry of Health issued Ministerial Order 665/2012 creating stroke referral services across the country20. Between 1998 and 2017, the country saw rapid expansion in the number of family health teams. Studies have shown an association between this expansion and reduced mortality from stroke21.
Despite the trends in IS and HS revealed by the present study, the number of deaths from stroke remain high across the country, with rates higher than those found in developed countries and some of the highest rates in Latin America22,23. According to the Global Burden of Disease study (GBD), the country with the highest mortality rate was Uruguay (102/100,000), followed by Brazil (60.47/100,000), Chile (57.95/100,000) and Argentina (57.78/100,000)24.
Our findings show that the only age group in which the proportion of deaths was higher among women than men was 80 years and over. De Souza et al. reported similar results in a study investigating stroke mortality between 1996 and 201525. These findings are also consistent with results found in North America and Europe26. Factors explaining higher mortality among women in this age group include increased susceptibility to systemic arterial hypertension (SAH) in postmenopausal women27 and atrial fibrillation, which is a risk factor that leads to a fourfold increase in the likelihood of IS in around 60% of women aged over 7528. The higher proportion of women in this age group can also be explained by the fact that women live longer than men29.
IS mortality rates were higher in older age groups while HS mortality was higher in the youngest age group. These findings are consistent with those reported by De Moraes et al., who studied stroke mortality in young patients (10-49 years) in the South and Southeast of Brazil, finding that 76% of deaths were from HS30. Risk of death from stroke is substantially higher in the older population than in other age groups. This can be partially explained by accumulation of risk factors in this group, such as SAH, diabetes, alcoholism, smoking and unhealthy eating habits31.
IS and HS mortality was higher in the Southeast and South, although the findings show a significant downward trend in these regions. In contrast, the North and Northeast showed lower rates with a significant upward trend. The higher rates in more developed states may be explained by the greater influence of chronic conditions in the mortality profile in these more populous regions25. In contrast, stroke mortality may be lower in more socially vulnerable regions because of mortality from other poverty-related diseases, such as infectious and parasitic diseases32.
Higher AAPC for IS mortality rates in the North and Northeast is consistent with the findings of a study by Mansur and Guimarães, who analyzed cardiovascular mortality in Brazil also using SIM data, although over a different period33,34. According to the authors, these regions show lower consumption of fruit and vegetables and regular exercise rates, and higher rates of physical inactivity, self-reported poor health and SAH35. In addition, according to the 2019 National Health Survey, people living in the Northeast have poorer access to at least one blood pressure medication in popular pharmacies and a higher prevalence of stroke36. Furthermore, the regions in Brazil with the highest prevalence of obesity are the North, Northeast and Midwest37. The combination of these factors results in increased risk for stroke38 and may explain the results found for these regions by the present study.
Unlike the findings reported by Lotufo et al., who found a 50% reduction in the proportion of deaths due cerebrovascular disease below 70 years of age between 1990 and 201539, our results show that reductions in IS mortality were highest in the 80 years and over group. The opposite was the case for HS mortality rates, which only increased in the oldest age group among men. These findings are also inconsistent with the results of a study by Passos et al.40, who found a substantial progressive decline in stroke mortality across all age groups. This may be explained by method differences, especially the stroke cause inclusion criteria. Other studies included all ICD-10 codes for stroke11,41, while the present study included only strokes that may cause brain damage which, in principle, are detectable by neuroimaging (IS and HS).
During the period 2015-2019, there was a reduction in the pace of decline in four of the six targets proposed in 201142 and now monitored under the Strategic Action Plan to Combat Chronic Diseases and Non-Communicable Diseases in Brazil 2021-2030: a 2% per year reduction in premature death (30-69 years) from noncommunicable diseases (NCDs); 30% decrease in smoking prevalence; stabilization of obesity among adults; and a 10% rise in the recommended consumption of fruit and vegetables43. This may partially explain the increase in IS mortality across all regions from 2015 observed in the present study.
Our findings reveal a reduction in HS mortality at national level, which is consistent with the results of a study by Oliveira GMM et al.44 using data from the 2017 GBD. However, it is important to highlight that disadvantaged regions such as the Northeast and North showed a positive AAPC in the 80 years and over age group and that APC in the North between 2011 and 2017 reached + 9.0.
The downward national trend in US mortality may be explained by improvements in the completion of death certificates45; however, US still account for most deaths from stroke recorded on death certificates in the SIM databases. This downward trend was less pronounced in the North and Northeast in the three youngest age groups, and an upward trend was observed in the 80 year and over group. Garritano et al. also found a higher proportion of deaths from US when compared with deaths from IS and HS, with rates of over 15% in older adults in the North11, while Jorge et al. found rates over 20% in the North and Northeast46. Key problems in these regions include poor access to health care related to their huge geographic area and cultural factors influencing behavior of local communities47. The quality of SIM data is also poorer in these regions48.
Deaths from US remain a challenge for estimating stroke mortality. Rolim and Martins investigated the use of cranial computed tomography (CT scan) in patients admitted to hospital with suspected stroke using data from the country’s hospital information system. The authors reported that the scan was not performed in 73% of cases, despite half of the admissions being to hospitals with a scanning machine. Paradoxically, not performing the scan was not the main factor used for classifying the condition as US, rather TC scans were more common in the group classified as not specified49.
Based on the literature, it is likely that the leading cause of death from stroke nationally and across regions is IS, due to the higher prevalence of this type50. The downward trend in US, slight increase in HS and substantial increase in IS mortality found by this study in the North and Northeast corroborate this hypothesis. As mentioned above, improvement in the quality of SIM data in these regions may have contributed to the downward trend in deaths from US, with a consequent rise in IS mortality.
The positive correlations found between all dimensions of the MHDI and the three groups of causes studied may be explained by the fact that in 2000 the largest variations in the index occurred in areas where it was lowest. These improvements were therefore not sufficient to cause a positive impact on mortality when the causes of stroke are analyzed alone. Positive correlations have been observed in medium-to-high-income countries but not in high-income countries, where HDIs have been higher for longer and access to specialized care services is better51.
The positive correlations between HS and US and income and employment and human capital suggest that mortality increases with increasing social vulnerability. These dimensions are linked mainly to education, employment and income, which are factors associated with stroke52. In contrast, there was a negative correlation between HS and US and infrastructure. The infrastructure dimension reflects access to basic sanitation services and urban mobility, which is linked to place of residence and has a significant impact on well-being and access to health services. These findings may reflect poor utilization of services due to lack of information about available services.
The main limitation of this study is the quality data in the SIM databases. However, only 198 deaths from stroke in our sample had missing information, corresponding to a less than 1% loss. This is a reflection of the efforts made by the Ministry of Health through partnerships with state and municipal governments to improve data quality, such as the Regional Inequalities Reduction Project, Infant Mortality Reduction Project in the Northeast and Legal Amazon and Ill-Defined Cause Reduction Project in 200553. This study is the first of its kind in Brazil to consider only ICD-10 codes for stroke with brain damage that is detectable by neuroimaging, thus exclusively encompassing IS and HS and excluding codes that represent risk factors that do not necessarily cause brain damage. This fact and the methods used to analyze the variables, particularly joinpoint regression, may explain the differences between our results and those of other studies on death from stroke.

Conclusions
Although in general stroke mortality in Brazil showed a downward trend during the period 2000-2019, the results of the analysis by groups of causes, regions and age group reveal distinct realities in a country with continental proportions and deep inequalities. In disadvantaged regions (the North and Northeast), the findings show an upward trend in IS and HS mortality, with increases being more pronounced in individuals aged over 60. Progressive increases in the MHDI and decreases in the SVI across the country have yet to have an impact on mortality rates, especially in disadvantaged regions. In contrast to the stabilization of IS mortality, the downward trend in US appears to reflect improvements in SIM data quality.


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