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  • Research note
  • Open Access

Correlation between municipal human development index and stroke mortality: a study of Brazilian capitals

  • 1Email author,
  • 1,
  • 1,
  • 1,
  • 1,
  • 2,
  • 2,
  • 3 and
  • 1
BMC Research Notes201811:540

https://doi.org/10.1186/s13104-018-3626-9

  • Received: 15 May 2018
  • Accepted: 20 July 2018
  • Published:

Abstract

Objective

To analyze the correlation between municipal human development indices (MHDIs) and stroke mortality in residents of Brazilian state capitals in 2010. A secondary data analysis was conducted in 2015 using data for the MHDI and the following dimensions: income, longevity and education which were obtained from the United Nations Development Program. Additionally, we analyzed age-standardized stroke mortality data from the Department of System Information Unified Health of Brazil.

Results

We observed a correlation between stroke mortality and MHDIs overall (Pearson r = − 0.563; p = 0.002) and within the following dimensions: income (Spearman’s ρ = − 0.479; p = 0.011), longevity (Pearson r = − 0.510; p = 0.006) and education (Pearson r = − 0.592; p = 0.001). We identified moderate but significant negative correlations between MHDI overall and in its individual dimensions (income, longevity, and age) and stroke mortality in Brazilian capitals. Stroke is the second leading cause of death in industrialized countries and the leading cause of death in Brazil. Therefore, the discovery of factors that may influence the epidemiology of stroke is important for the construction of adequate policies considering to the socioeconomic status in these places and with an emphasis in lower socioeconomic status places.

Keywords

  • Stroke
  • Epidemiology
  • Socioeconomic status

Introduction

Each year, strokes are suffered by approximately 15 million people worldwide. Of these persons, approximately 5 million die [1]; it is estimated that by 2030, stroke may represent the sixth leading cause of disability-adjusted life-years lost in the world [2, 3]. Although the number of strokes is alarming, a gradual decrease in the occurrence of stroke has been observed in developed countries. This gradual decrease is likely due to better systemic arterial hypertension (SAH) control and a decrease in the levels of smoking in these populations [1].

Most fatal stroke cases occur among men aged from 45 to 59 years who live in countries with lower levels of socioeconomic development, such as Caribbean countries and former socialist countries in Europe [4]. In Brazil, a country that is considered developing [5], the prevalence of stroke has decreased. However, the disease rate has not decreased as fast as it has in developed nations [6, 7].

Studies of the association between socioeconomic status and stroke risk in adults have shown conflicting conclusions, demonstrating both positive and negative correlations [8, 9]. Recent studies [10, 11] regarding the effect of different socioeconomic statuses throughout the life span have shown that children of lower socioeconomic status may be at higher risk of stroke in adulthood. In parallel, higher stroke prevalence rates have been observed in African countries than in developed nations [12].

Human development indexes (HDIs) represent one mechanism by which to analyze socioeconomic status and are also the most frequently used indicator of the socioeconomic level of a nation [13]. These indexes are in-depth measures of not only the economic profile but also quality of life, longevity, health services and education in a nation [13]. However, few studies have analyzed the role of HDI in the epidemiology of stroke both in Brazil and worldwide [4].

Although Brazil has a large population and vast geographic plurality, few studies have been conducted to generate understanding regarding the effect of differences in income distribution, education and longevity on health indicators [14]. One 2013 study [4] also identified a correlation between stroke mortality and HDIs in the global context. Analyses using the HDI dimensions may serve as important instruments in understanding the associations between mortality, income, longevity and education.

Thus, the objective of this study was to analyze the correlations between MHDIs and its dimensions for income, education and longevity with the stroke mortality in Brazilian state capitals.

Main text

Materials and methods

A secondary data analysis was conducted using municipal human development index (MHDI) and stroke mortality data for Brazilian state capitals.

HDI data from Brazilian state capitals in 2010 was collected from the United Nations Development Program (UNDP—http://www.pnud.org.br). The UNDP website, which is maintained by the United Nations, includes a virtual atlas that may be used to analyze and quantify the rate of development in municipalities.

The HDI is calculated based on indicators of living on a healthy and long life (longevity); access to knowledge, as measured by various factors (education); and standard of living, as indicated by Gross National Income [15]. We decided to analyze MHDI data from only 2010 due the implementation of a new calculation method and non-availability data after this year.

Stroke mortality data were collected from the Mortality Information System (Sistema de Informação de mortalidadeSIM) of the Department of the Brazilian Unified Health System Information (DATASUS—http://www.datasus.gov.br), which is a demographic, social and health database maintained by the Brazilian Ministry of Health.

Stroke was defined according to International Diseases Classification codes (10th edition; ICD-10) (OMS 1997) and included subarachnoid hemorrhage (I60), intracranial hemorrhage (I61), cerebral infarction (I63) and stroke not specified as ischemic and/or hemorrhagic (I64).

The gross stroke mortality rate was calculated by dividing the number of deaths due to stroke in Brazilian capitals by the total population living in each capital and multiplying the result by 100,000 inhabitants, and this measure was then standardized by the age of the population using the direct standardization method of the World Health Organization [16].

Data were independently collected using data collection forms by two different researchers. Subsequently, the data were validated, and the disagreements were independently resolved by a third researcher.

Descriptive statistics were performed, and absolute and relative frequencies were calculated. Spearman’s (ρ) correlation tests were performed on the MHDI income variable, as it did not demonstrate a normal distribution (Shapiro–Wilk test, p < 0.05), and Pearson’s correlation (r) was used for the MHDI dimensions, education and longevity, as the evaluated data demonstrated normality, as indicated by the Shapiro–Wilk test (p > 0.05). The confidence level was 95%. Stata 11.0 was used for the statistical analyses.

Results

Table 1 describes stroke age-standardized mortality (per 100,000 inhabitants), MHDI and its dimensions (income, education and longevity). We observed higher rates of age-standardized stroke mortality in southeastern region capitals. We also observed higher HDIs and income, longevity and education in southern region capitals.
Table 1

Description of stroke age-standardized mortality (per 100,000 inhabitants), MHDI and its dimensions by capitals of each region

Capital per region

Stroke age-standardized mortality (per 100,000 inhabitants)

MHDI

MHDI

Income

Education

Longevity

North

 Porto Velho

48.60

0.736

0.764

0.638

0.819

 Rio Branco

47.78

0.727

0.729

0.661

0.798

 Manaus

33.67

0.737

0.738

0.658

0.826

 Boa Vista

36.33

0.752

0.737

0.708

0.816

 Belem

55.39

0.746

0.751

0.673

0.822

 Macapá

46.34

0.733

0.723

0.633

0.820

 OPalmas

39.57

0.788

0.789

0.749

0.827

Northeast

 São Luis

38.55

0.768

0.741

0.752

0.813

 Teresina

38.50

0.751

0.731

0.707

0.820

 Fortaleza

29.60

0.754

0.749

0.695

0.824

 Natal

25.99

0.763

0.768

0.694

0.835

 João Pessoa

38.47

0.763

0.770

0.693

0.832

 Recife

27.09

0.772

0.798

0.698

0.825

 Maceió

43.44

0.721

0.739

0.635

0.799

 Aracaju

27.28

0.770

0.784

0.708

0.823

 Salvador

35.20

0.759

0.772

0.679

0.835

Southeast

 Belo Hoiizonte

30.69

0.810

0.841

0.737

0.856

 Vitória

32.69

0.845

0.876

0.805

0.855

 Rio de Janeiro

34.79

0.799

0.840

0.719

0.845

 São Paulo

37.98

0.805

0.843

0.725

0.855

South

 Curitiba

26.88

0.823

0.850

0.768

0.855

 Florianópolis

20.45

0.847

0.870

0.800

0.873

 Porto Alegre

43.86

0.805

0.867

0.702

0.857

Center West

 Campo Grande

35.63

0.784

0.790

0.724

0.844

 Cuiabá

38.44

0.785

0.800

0.726

0.834

 Goiânia

30.99

0.799

0.824

0.739

0.838

 Brasilia

32.07

0.824

0.863

0.742

0.873

In addition, there were some capitals that stood out as having higher stroke mortality rates than other capitals in the same region. In the southern region, Porto Alegre, which had an MHDI of 0.805 (55.51 deaths per 100,000 inhabitants), had approximately twice the age-standardized mortality rate as other capitals in the same region. The other capitals in the same region, Curitiba and Florianópolis, had MHDIs of 0.823 and 0.847, respectively.

Furthermore, in the northern region, Belém stood out due to its age-standardized mortality rate of 42.47 per 100,000 inhabitants. Some other capitals in the northern region, Porto Velho, Rio Branco and Macapá, had substantial age-standardized mortality rates of 48.6, 47.78, and 46.34, respectively.

In the northeastern region, Maceió stood out as having a higher age-standardized mortality rate (43.44) and lower HDI overall (0.721) and within the income (0.739), education (0.635) and longevity (0.799) dimensions. On the other hand, Recife, which is also located in the northeastern region, has a low rate of age-standardized mortality (27.09) and a high HDI (0.772) overall and in the dimensions for income (0.798) and longevity (0.825).

Capitals in the southeastern region did not demonstrate large disparities in the association between mortality and HDI. However, Vitória had higher socioeconomic development and a lower age-standardized stroke mortality rate (32.69) when compared with São Paulo (37.98) and Rio de Janeiro (34.79), some of the most populated cities in Brazil.

In the central western region, Goiânia and Brasília had lower mortality rates (30.99 and 32.07, respectively) and HDIs (0.799 and 0.824, respectively). Cuiabá had a higher mortality rate (38.44) and, curiously, the lowest longevity MHDI (0.834).

The overall HDI presented a negative, moderate and significant correlation (Pearson r = − 0.593, p = 0.002) with age-standardized stroke mortality in Brazilian capitals. This correlation was also observed for the HDI dimensions, income (Spearman’s ρ = − 0.479, p = 0.011) education (Pearson r = − 0.592, p = 0.001) and longevity (Pearson r = − 0.510, p = 0.006) (Fig. 1).
Fig. 1
Fig. 1

Relationship between human development indexes and stratification for income, education and longevity with stroke mortality in Brazilian capitals

Discussion

In our analysis of the correlation between stroke mortality and municipal HDI, we observed a moderate and inverse correlation, which was also identified mainly between the education dimension of the MHDI and age-standardized stroke mortality.

Stroke is the second leading cause of death in industrialized countries and the leading cause of death in Brazil [17, 18]. Some risk factors for this disease, such as systemic arterial hypertension (SAH), obesity and hyperlipidemia, are related to unhealthy living standards [19]. Smoking is more often identified in populations of low socioeconomic status and nations with lower HDIs [20].

A negative correlation was identified between mortality due to stroke and income. This finding may be is because individuals with greater financial capacity receive more substantial and qualified health services, resulting in better treatment and adequate stroke prevention [21].

This Brazilian situation can be represented by Basic Health Units (Unidade Básica de Saúde—UBS), which are important in order for stroke risk factors, such as SAH and Diabetes Mellitus, to be controlled and prevented. Neto et al. demonstrated that regions with higher MHDIs had units with better services and high-quality infrastructure [22].

On the other hand, some studies have shown that countries with low and medium HDIs have different characteristics when compared with developed countries. In these nations, people of higher socioeconomic status have a higher risk of stroke death than do those of lower status [8].

Higher incomes may be associated with higher education, which may be associated with lower stroke mortality.

Thus, there are studies [11, 23] that have demonstrated, using longitudinal data, the presence of higher stroke mortality rates in individuals with low education levels. This pattern may be explained by the fact that individuals of higher socioeconomic status have better access to education, which may result in a better understanding of health, resulting in a lower stroke risk [21, 24].

At the same time, many studies have demonstrated an association between national educational development and various cancers incidence rates [25, 26], indicating that HDIs may play a role in non-communicable disease epidemiology.

Populations in regions with higher socioeconomic levels may have better access to education and qualified health services [27]. The availability of qualified health services facilitates and improves the possibility of early diagnosis of chronic diseases, such as cancer. These services may also reduce the lethality associated with these diseases, increasing population longevity [28].

Countries with higher HDIs may have a greater investment in health infrastructure and education and access to modern screening and treatment programs [27]. These measures may explain the longer life expectancies identified in these populations, even though age represents a risk factor for the development of cerebrovascular diseases [29].

Developing regions have been found to have higher rates of mortality from stroke. This finding may be explained by the gradual increase in life expectancy and increasingly westernized lifestyle [28, 30], which may be associated with habits such as smoking, alcohol consumption and physical inactivity [31].

The negative correlation identified between stroke and the socioeconomic indicator HDI was also observed in its dimensions: income, longevity and education.

Limitations

This study has some limitations. The ecological data were susceptible to confounding, reverse causality and ecological fallacy and it is possible that associations at the individual level differ than at the group level. Stroke mortality data may suffer from some bias due the underestimation of mortality in data from the Mortality Information System (SIM/DATASUS). However, this system has been found to have good coverage [32, 33], which is estimated to be 7% by the proportion of poorly defined deaths [34].

Declarations

Authors’ contributions

DMML and FWSF contributed to concept, writing and revising the manuscript. FA, TCCA, LSP, LVAS, SJG, ESM and JAC contributed to writing, data collect, statistical analysis and revising the manuscript. FA, FWSF, DMML and LSP participated in the statistical analysis. All authors read and approved the final manuscript.

Acknowledgements

None.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The datasets generated during and/or analyzed during the current study are not publicly available due, however it can be requested to the corresponding author on reasonable request.

Consent to publish

Not applicable.

Ethics approval and consent to participate

Not applicable.

Funding

Programa Institucional de Bolsas de Iniciação Científica—PIBIC (Notice 2015–2016). Institutional Program for Scientific Initiation Scholarship.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Laboratório de Epidemiologia e Análise de Dados, Faculdade de Medicina do ABC, Santo André, São Paulo, Brazil
(2)
Disciplina de Angiologia e Cirurgia Vascular, Faculdade de Medicina do ABC, Santo André, Brazil
(3)
Universidade Federal do Tocantins, Palmas, Brazil

References

  1. Sofer D. Study assesses the global stroke burden. Am J Nurs. 2016;116(9):16.View ArticleGoogle Scholar
  2. Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006;3(11):e442.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Johnston SC, Mendis S, Mathers CD. Global variation in stroke burden and mortality: estimates from monitoring, surveillance, and modelling. Lancet Neurol. 2009;8(4):345–54.View ArticlePubMedGoogle Scholar
  4. Wu SH, Woo J, Zhang XH. Worldwide socioeconomic status and stroke mortality: an ecological study. Int J Equity Health. 2013;12:42.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Rodrigues-Júnior AL, Ruffino-Netto A, Castilho EA. Spatial distribution of the human development index, HIV infection and AIDS–tuberculosis comorbidity: Brazil, 1982–2007. Revista Brasileira de Epidemiologia. 2014;17:204–15.View ArticlePubMedGoogle Scholar
  6. Garritano CR, Luz PM, Pires ML, Barbosa MT, Batista KM. Analysis of the mortality trend due to cerebrovascular accident in Brazil in the XXI century. Arq Bras Cardiol. 2012;98(6):519–27.View ArticlePubMedGoogle Scholar
  7. McCarron P, McCarron MO, Murray L, Kee F. Secular trends in stroke mortality and early-life environment. Lancet. 2003;361(9362):1059–60.View ArticlePubMedGoogle Scholar
  8. Walker RW, McLarty DG, Kitange HM, Whiting D, Masuki G, Mtasiwa DM, et al. Stroke mortality in urban and rural Tanzania. Adult morbidity and mortality project. Lancet. 2000;355(9216):1684–7.View ArticlePubMedGoogle Scholar
  9. Steenland K, Hu S, Walker J. All-cause and cause-specific mortality by socioeconomic status among employed persons in 27 US states, 1984–1997. Am J Public Health. 2004;94(6):1037–42.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Johnson RC, Schoeni RF. Early-life origins of adult disease: national longitudinal population-based study of the United States. Am J Public Health. 2011;101(12):2317–24.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Glymour MM, Avendaño M, Haas S, Berkman LF. Lifecourse social conditions and racial disparities in incidence of first stroke. Ann Epidemiol. 2008;18(12):904–12.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Matenga J, Kitai I, Levy L. Strokes among black people in Harare, Zimbabwe: results of computed tomography and associated risk factors. Br Med J. 1986;292(6536):1649–51.View ArticleGoogle Scholar
  13. Desenvolvimento. PBPdNUpo. Atlas do Desenvolvimento Humano no Brasil. 2003.Google Scholar
  14. Messias E. Income inequality, illiteracy rate, and life expectancy in Brazil. Am J Public Health. 2003;93(8):1294–6.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Liberatos P, Link BG, Kelsey JL. The measurement of social class in epidemiology. Epidemiol Rev. 1988;10:87–121.View ArticlePubMedGoogle Scholar
  16. Ahmad OB, Boschi-Pinto C, Lopez AD, Murray CJ, Lozano R, Inoue M. Age standardization of rates: a new WHO standard. 2001. p. 1–14.Google Scholar
  17. Cabral NL. Epidemiologia e impacto da doença cerebrovascular no Brasil e no mundo. ComCiência. 2009. http://comciencia.scielo.br/scielo.php?script=sci_arttext&pid=S1519-76542009000500010&nrm=iso.
  18. Schmidt MI, Duncan BB, Silva AG, Menezes AM, Monteiro CA, Barreto SM, et al. Chronic non-communicable diseases in Brazil: burden and current challenges. Lancet. 2011;377(9781):1949–61.View ArticlePubMedGoogle Scholar
  19. Kim S, Symons M, Popkin BM. Contrasting socioeconomic profiles related to healthier lifestyles in China and the United States. Am J Epidemiol. 2004;159(2):184–91.View ArticlePubMedGoogle Scholar
  20. Rafiemanesh H, Mehtarpour M, Khani F, Hesami SM, Shamlou R, Towhidi F, et al. Epidemiology, incidence and mortality of lung cancer and their relationship with the development index in the world. J Thorac Dis. 2016;8(6):1094–102.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Müller-Nordhorn J, Nolte CH, Rossnagel K, Jungehülsing GJ, Reich A, Roll S, et al. Knowledge about risk factors for stroke: a population-based survey with 28,090 participants. Stroke. 2006;37(4):946–50.View ArticlePubMedGoogle Scholar
  22. Soares Neto JJ, Machado MH, Alves CB. O Programa Mais Médicos, a infraestrutura das Unidades Básicas de Saúde e o Índice de Desenvolvimento Humano Municipal. Ciência Saúde Coletiva. 2016;21:2709–18.View ArticleGoogle Scholar
  23. Hajjar I, Kotchen T. Regional variations of blood pressure in the United States are associated with regional variations in dietary intakes: the NHANES-III data. J Nutr. 2003;133(1):211–4.View ArticlePubMedGoogle Scholar
  24. Wardle J, Steptoe A. Socioeconomic differences in attitudes and beliefs about healthy lifestyles. J Epidemiol Community Health. 2003;57(6):440–3.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Pakzad R, Mohammadian-Hafshejani A, Mohammadian M, Pakzad I, Safiri S, Khazaei S, et al. Incidence and mortality of bladder cancer and their relationship with development in Asia. Asian Pac J Cancer Prev. 2015;16(16):7365–74.View ArticlePubMedGoogle Scholar
  26. Ghoncheh M, Mohammadian-Hafshejani A, Salehiniya H. Incidence and mortality of breast cancer and their relationship to development in Asia. Asian Pac J Cancer Prev. 2015;16(14):6081–7.View ArticlePubMedGoogle Scholar
  27. Hu K, Lou L, Tian W, Pan T, Ye J, Zhang S. The outcome of breast cancer is associated with national human development index and health system attainment. PLoS ONE. 2016;11(7):e0158951.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Bray F, Jemal A, Grey N, Ferlay J, Forman D. Global cancer transitions according to the human development index (2008–2030): a population-based study. Lancet Oncol. 2012;13(8):790–801.View ArticlePubMedGoogle Scholar
  29. Lackland DT, Roccella EJ, Deutsch AF, Fornage M, George MG, Howard G, et al. Factors influencing the decline in stroke mortality. A statement from the American Heart Association/American Stroke Association. 2013.Google Scholar
  30. Razi S, Ghoncheh M, Mohammadian-Hafshejani A, Aziznejhad H, Mohammadian M, Salehiniya H. The incidence and mortality of ovarian cancer and their relationship with the human development index in Asia. Ecancermedicalscience. 2016;10:628.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Chang T, Gajasinghe S, Arambepola C. Prevalence of stroke and its risk factors in Urban Sri Lanka: population-based study. Stroke. 2015;46(10):2965–8.View ArticlePubMedGoogle Scholar
  32. Ministério da saúde. Informações de saúde TABNET - Estatísticas vitais. Datasus. Brasil; 2018. http://tabnet.datasus.gov.br/cgi/tabcgi.exe?sim/cnv/obt10br.def
  33. Figueiroa BD, Vanderlei LC, Frias PG, Carvalho PI, Szwarcwald CL. Analysis of coverage in the mortality information system in Olinda, Pernambuco State, Brazil. Cad Saude Publica. 2013;29(3):475–84.View ArticleGoogle Scholar
  34. Adami F, Figueiredo FW, LaS Paiva, Sá TH, Santos EF, Martins BL, et al. Mortality and incidence of hospital admissions for stroke among Brazilians aged 15 to 49 years between 2008 and 2012. PLoS ONE. 2016;11(6):e0152739.View ArticlePubMedPubMed CentralGoogle Scholar

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