Mortality inequalities measured by socioeconomic indicators in Brazil: a scoping review

Ichihara, Maria Yury; Ferreira, Andrêa J.F.; Teixeira, Camila S. S.; Alves, Flávia Jôse O.; Rocha, Aline Santos; Diógenes, Victor Hugo Dias; Ramos, Dandara Oliveira; Pinto Júnior, Elzo Pereira; Flores-Ortiz, Renzo; Rameh, Leila; Costa, Lilia Carolina C. da; Gonzaga, Marcos Roberto; Lima, Everton E. C.; Dundas, Ruth; Leyland, Alastair; Barreto, Maurício L.

doi:10.11606/s1518-8787.2022056004178

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Review • Rev. Saúde Pública 56 • 2022 • https://doi.org/10.11606/s1518-8787.2022056004178 copy

Mortality inequalities measured by socioeconomic indicators in Brazil: a scoping review

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

OBJECTIVE

Summarize the literature on the relationship between composite socioeconomic indicators and mortality in different geographical areas of Brazil.

METHODS

This scoping review included articles published between January 1, 2000, and August 31, 2020, retrieved by means of a bibliographic search carried out in the Medline, Scopus, Web of Science, and Lilacs databases. Studies reporting on the association between composite socioeconomic indicators and all-cause, or specific cause of death in any age group in different geographical areas were selected. The review summarized the measures constructed, their associations with the outcomes, and potential study limitations.

RESULTS

Of the 77 full texts that met the inclusion criteria, the study reviewed 24. The area level of composite socioeconomic indicators analyzed comprised municipalities (n = 6), districts (n = 5), census tracts (n = 4), state (n = 2), country (n = 2), and other areas (n = 5). Six studies used composite socioeconomic indicators such as the Human Development Index, Gross Domestic Product, and the Gini Index; the remaining 18 papers created their own socioeconomic measures based on sociodemographic and health indicators. Socioeconomic status was inversely associated with higher rates of all-cause mortality, external cause mortality, suicide, homicide, fetal and infant mortality, respiratory and circulatory diseases, stroke, infectious and parasitic diseases, malnutrition, gastroenteritis, and oropharyngeal cancer. Higher mortality rates due to colorectal cancer, leukemia, a general group of neoplasms, traffic accident, and suicide, in turn, were observed in less deprived areas and/or those with more significant socioeconomic development. Underreporting of death and differences in mortality coverage in Brazilian areas were cited as the main limitation.

CONCLUSIONS

Studies analyzed mortality inequalities in different geographical areas by means of composite socioeconomic indicators, showing that the association directions vary according to the mortality outcome. But studies on all-cause mortality and at the census tract level remain scarce. The results may guide the development of new composite socioeconomic indicators for use in mortality inequality analysis.

Mortality, trends; Geographic Locations, epidemiology; Socioeconomic Factors; Health Status Disparities; Review

Characterization	n	%
Year of publication
2000–2010	12	50.0
2011–2020	12	50.0
Geographical coverage of the study
Country	4	16.7
State	4	16.7
Municipality	16	66.6
Area level of socioeconomic inequity indicators
Country	2	8.3
State	2	8.3
Municipality	6	25.0
Districts	5	20.8
Census tracts	4	16.6
Others	5	20.8
Mortality outcomes^a
All-cause mortality	2	8.3
Cause-specific (may be broken down, according to results)	19	79.2
Age-specific mortality outcomes	11	45.8

Author(s)/year	Composite indicator	Variables/domains	Source/year of variables/domains	Mortality measure	Main findings
Country
Machado et al.³⁷ (2019)	Human Development Index (HDI)	Income, schooling, and longevity	Not specified	Suicide, homicide, and road traffic accidents mortality rates	HDI mortality rates were most evident in the poorest quintiles.
Alarcão et al.³⁹ (2020)	Human Development Index and Social Vulnerability Index	Income, schooling, and longevity	Not specified	Age-specific suicide mortality rate (15–19, 20–24, and 25–29 years old)	Socioeconomic deprivation was an important determinant of suicide in younger people and significantly influenced high-risk groups for suicide mortality rates.
State
Guimarães et al.⁴⁰ (2013)	Socioeconomic status	Gross Domestic Product per capita; average household income per capita; Gini Index; adults with family income < 1/2 minimum wage	Not specified	Age-adjusted mortality rate (≥ 20 years old) for colorectal cancer	Mortality rates according to gender were directly related to lower socioeconomic status.
Ribeiro et al.²⁷ (2007)	Social Exclusion Index (SEI)	Poverty, employment, income, literacy, years of schooling, population aged ≤ 19 years, and violence	Brazilian Demographic Census (2000)	Age-adjusted leukemia mortality rate (birth to 4, 5–9, 10–14, and 15–19 years old)	Social inequality was negative correlated with leukemia mortality rates in both genders; higher significant decreases in the more developed states.
Municipal
Schuck-Paim et al.³⁸ (2019)	Human Development Index	Income, schooling, and longevity	Not specified	Pneumonia mortality rate (≤ 59 months)	Pneumonia mortality rate was declined modestly and statistical significantly in municipalities with a high percentage of extreme childhood poverty, and a higher proportion of low maternal schooling.
Drachler et al.³⁴ (2014)	Social Vulnerability Index (IVS-5)	Household conditions: income, water distribution, garbage collection, bathroom, illiteracy in people aged >15, and overcrowding	Brazilian Demographic Census (2010)	Child mortality rate	The most vulnerable municipalities had higher hospitalization rates for sensitive. Primary care conditions, and a higher infant mortality rate than the least vulnerable municipalities.
Medeiros et al.²⁸ (2012)	Socioeconomic Development Index (Índice de Desenvolvimento Socioeconômico - IDESE)	Four thematic blocks: schooling; income; sanitation and household conditions; and health	Brazilian Demographic Census (2001)	CVD mortality rate (ischemic, hypertensive, and cerebrovascular)	Direct relationship between average mortality rate and IDESE in municipalities with 5,000–15,000 inhabitants. IDESE variables only partially explained the differences in CVD mortality rates in socioeconomically similar municipalities.
Faria and Santana³⁵ (2016)	Material Deprivation Index (Índice de Privação Material - IPM)	Illiteracy among women of reproductive age, households without indoor sanitary facilities, and unemployment	Brazilian Demographic Census (2010)	Infant mortality rate (< 1 year)	High infant mortality rates in municipalities with high material deprivation.
Alves et al.⁴¹ (2020)	Social Determination Indicator	Dimension 1: vulnerable to poverty and schooling; Dimension 2: household income	Municipal Department of Health Surveillance	Tuberculosis mortality risk	A worse social condition, such as low schooling levels and poverty, increased tuberculosis mortality risk by three times.
Bonfim et al.³⁶ (2020)	Social Deprivation Index (SDI)	Household conditions, no nominal monthly income, and illiterate head of household	Brazilian Demographic Census (2010)	Fetal and infant mortality rate	High fetal and infant mortality rates found in areas with poor living conditions, where SDI showed spatial dependence (I = 0.18; p = 0.014) of clusters.
Districts
Silva et al.²⁹ (2008)	Composite Social Deprivation Indicator (Indicador Composto de Carência Social – ICS)	Household conditions, schooling, and income	Brazilian Demographic Census (2000)	All-cause, cause-specific, and age-adjusted (> 60 years old) mortality rate	Positive correlation between social deprivation and cause-specific deaths. Districts with extreme social deprivation had a 2.9 times higher risk of death due to traffic accidents, and 3.9 times higher risk of pneumonia in older adults.
Araújo et al.⁴⁵ (2005)	Socioeconomic condition	Healthcare unit, urban infrastructure and services, safety, schooling, household building pattern, and afforestation	Municipal Planning Secretariat (1999)	External cause mortality rate	Spatial distribution of external cause mortality rates showed differences in socioeconomic levels. Risk of death from homicides and traffic accidents was higher in the low and medium-low socioeconomic strata. Areas in the middle and high socioeconomic strata presented higher mortality rates from traffic accidents.
Bassanesi et al.³⁰ (2008)	Socioeconomic condition	Education, population income, density, external cause mortality rate, aging and fertility rates, and infant mortality	Brazilian Demographic Census (2000)	Average CVD mortality rate (ischemic and cerebrovascular)	Early CVD mortality rate was 2.6 times higher in districts classified as the worst social stratum. Among districts in the most extreme deprivation strata, RR reached 3.3 for CVD, and 3.9 for cerebrovascular diseases. 62% of early deaths were in the worst stratum.
Campos et al.⁴² (2000)	Socioeconomic composition of districts	Houses with a single-family; households with infrastructural conditions; head of household monthly income, and favela census tracts related to the total number of sectors in each neighborhood	Department of Health Information censuses and maps - Oswaldo Cruz Foundation (1995)	Infant mortality rate (neonatal and post-neonatal) and proportional mortality by cause groups	Infant mortality rate showed a dispersed spatial distribution, without direct relation to the socioeconomic profile. The flow of children between their residences and place of death shows a movement that starts in the most impoverished areas towards the wealthier ones, which have a greater number of health facilities.
Oliveira et al.³¹ (2010)	Composite Deprivation Index	Head of the family’s income and schooling, and household conditions	Brazilian Demographic Census (2000)	Standardized cause-specific mortality rate (circulatory system, neoplasms, respiratory system, deaths from external causes, perinatal infections, and infectious and parasitic diseases)	Most cases of aggression (86%) occurred in the most deprived groups. There was no statistically significant correlation between socioeconomic levels and perinatal, cancer, respiratory, or parasitic mortality.
Census tract
Peres et al.²¹ (2011)	Social Exclusion/Inclusion Index (Índice de Exclusão/Inclusão Social – IEI)	1. Autonomy: income, employment, and destitution; 2. Quality of life: access to basic services, housing infrastructure and travel; 3. Human development: schooling, longevity, and risk of death; 4. Equity: income and literacy of women heads of households	Brazilian Demographic Census (2000) and other national and municipal sources: SEADE (2000), PRO-AIM (2000), Fipe (2000), Embraesp (2000), and Metro (1997)	All-cause homicide mortality rate and by type of weapon, gender, race/skin color, age, and areas of social exclusion/inclusion	The gradient of homicide mortality rates increased as the degree of social exclusion increased. There was a very sharp decline in homicide mortality rates in extreme and high exclusion (-79.3% and -71.7%, respectively). There was also a decline in the mean and degree of social exclusion (-59.1% and -61.9%, respectively).
Vilela et al.²² (2008)	Social Deprivation Indicator (Indicador de Carência Social – ICS)	Household conditions and schooling/family head’s income	Brazilian Demographic Census (2000)	Infant mortality rate from infectious and parasitic diseases as the underlying and/or associated cause of death	There was a 48% higher risk of children under one year dying from infectious and parasitic diseases in the stratum of highest social deprivation.
Antunes et al.²³ (2008)	Socioeconomic status	Unemployment, insufficient schooling, family head’s academic qualifications, and Human Development Index	Brazilian Demographic Census (2000)	Oral and pharyngeal cancer mortality rate stratified by gender, age, year, underlying cause, and inner-city area of residence	The distribution of rate terciles at the area level highlighted a spatial gradient of mortality in the city. In the poorer areas (second and third terciles), higher mortality rates prevailed.
Silveira and Junger²⁴ (2018)	Social Development Index	Household conditions, illiteracy among people aged 10 to 14, and family head’’ income	Brazilian Demographic Census (2010)	Ischemic heart disease and cerebrovascular disease mortality rate	Greener sectors (third quartile) had 6.7% (95%CI: 3.5–9.8) and 4.7% (95%CI: 1.2–8.0) less mortality due to ischemic heart disease and cerebrovascular disease, respectively. Protective effect of green space was more significant for lower socioeconomic status (8.6%; 95%CI: 1.8–15.0). For cerebrovascular disease mortality rate, a protective effect was observed at the lowest socioeconomic levels (9.6%; 95%CI: 2.3–16.5).
Others
Bastos et al.³² (2009)	Urban Quality Index (Índice de Qualidade Urbana - IQU)	Schooling, income, housing infrastructure, and urban services infrastructure	Brazilian Demographic Census (2000)	External cause mortality rate (traffic accident, homicide, and suicide)	Homicide victims were young, black, male, residing in the poorest urban areas, and had lower IQU values. Suicides and traffic accidents affected older adults, white women, and residents of the wealthiest areas, with the highest IQU scores.
Belon and Barros³³ (2011)	Global Socioeconomic Level Score	Family head’s income and schooling	Brazilian Demographic Census (2000)	Life expectancy	Life expectancy for men and women was 6.9 and 5.5 years lower in impoverished areas than in areas of higher socioeconomic status. Social inequalities in life expectancy at birth decreased between 2000– 2005 as groups of lower socioeconomic status gained more years of life.
Teixeira et al.²⁵ (2002)	Index of Living Conditions (Índice de Condições de Vida - ICV)	Income, schooling, overcrowding, sanitation, and subnormal clusters	Brazilian Demographic Census (1991)	Mortality due to infectious and parasitic diseases: proportional mortality, mortality rate, and the standardized and specific mortality ratio	The highest mortality rates due to infectious and parasitic diseases occurred in poorer living conditions.
Macedo et al.²⁶ (2001)	Living Condition Status	Economic capital (income in minimum salaries); cultural capital (head of family’s schooling level)	Brazilian Demographic Census (1991)	Homicide mortality rate	The highest homicide mortality rate was found in the city’s most impoverished areas. Estimated homicide related to the risk of death was 2.9 (1991) in the worst living conditions, and 5.1 (1994) for better conditions.
Lotufo and Benseñor⁴⁴ (2009)	Social Exclusion Index	Concentration of young people, literacy, years of schooling, formal employment, violence, and inequality	Not specified	Stroke mortality by gender	Odds of death by stroke was 2.0 times higher in districts with higher social exclusion. A similar pattern was found for ischemic and hemorrhagic strokes in both genders. It had a negative correlation between income and proportional stroke mortality.

Author(s)/ Year	Limitations
Peres et al.²¹ (2011)	Lack of temporal data on the potential social determinants of homicide decline made it impossible to infer its causes. There were no discussions on the limitations resulting from the social exclusion/inclusion index.
Vilela et al. (2008)²²	The limitations considered were intra-aggregate heterogeneity, inter-group mobility, and the underreporting of infant deaths.
Antunes et al. (2008)²³	Different ways of measuring variables in statistical censuses in Barcelona and São Paulo. As an ecological study, it does not consider the relevant variation in individual socioeconomic characteristics. Another limitation is the relatively simple analytical scheme, which disregards non-linear relationships between mortality and socioeconomic status.
Silveira and Junger²⁴ (2018)	Use of secondary data is a limiting factor in this study, as is the possibility of ecological studies.
Machado et al.³⁷ (2019)	The short period analyzed.
Schuck-Paim et al.³⁸ (2019)	Despite the synthetic control method used to detect the benefits of pneumococcal conjugate vaccine introduction and explicitly designed to minimize confounding, the ecological study design may have disregarded other uncontrolled factors that can affect the estimates.
Medeiros et al²⁸ (2012)	Use of secondary data is a limiting factor in this study. Information may not be completely reliable and represents population averages as it is an ecological study.
Ribeiro et al.²⁷ (2007)	Ecological study that had to consider “ecological fallacy.” No individual assessment of socioeconomic status was performed in the study, and the smallest unit analyzed (state) was too large to represent a neighborhood effect.
Drachler et al.³⁴ (2014)	Does not mention limitations.
Silva et al.²⁹ (2008)	The associations found may be stronger due to spatial aggregation (neighborhood). The districts of Recife still have significant social heterogeneity, with wealth areas existing alongside pockets of poverty. Moreover, this study characterized mortality using an indicator created by other authors. Research shows that synthetic indicators do not capture the different nuances of social reality.
Bassanesi et al.³⁰ (2008)	Does not mention limitations.
Campos et al.⁴² (2000)	Does not mention limitations.
Oliveira et al.⁴³ (2010)	Considering the nature of the aggregate measure, a neighborhood classified with the highest deprivation does not always have the worst rates on all variables analyzed. The high variation in population composition (between 2,500 and 60,000 people) across districts was not considered. Regarding statistical analysis, no mortality smoothing techniques was performed, as it was not possible to assess the effect of deprivation on mortality. Use of the 2000 census to obtain socioeconomic indicators may result in limitations in understanding previous years.
Belon and Barros³³ (2011)	As a unit of analysis and area of residence, a limitation of this study is that its results do not necessarily reflect the situation of those belonging to each socioeconomic stratum.
Teixeira et al.²⁵ (2002)	Does not mention limitations.
Macedo et al.²⁶ (2001)	The stratification adopted in the study, although performed by aggregating similar zones, has several limitations due to the particular heterogeneity of the urban area of Salvador. Problems related to the quality of information were also studied.
Bastos et al.³² (2009)	An important limitation of ecological studies is that the relationship between two variables does not necessarily reflect the situation under study. Administrative regions may have caused degrees of heterogeneity due to the specific characteristics of each neighborhood.
Faria and Santana (2016)³⁵	Use of secondary data can be considered a limiting factor in this study.
Lotufo and Benseñor⁴⁴ (2009)	Does not mention limitations.
Araújo et al.⁴⁵ (2005)	Lack of data on living conditions disaggregated by neighborhood prevented the generation of a weighted indicator for classification according to more specific sociodemographic variables. Moreover, the quality of violent death records restricted a more comprehensive understanding.
Alves et al.⁴¹ (2020)	Limiting factors comprise the use of secondary data and the fact that deaths due to more severe forms of the outcome were not verified.
Guimarães et al.⁴⁰ (2013)	An ecological design that needed to measure the variables as proxies: income does not directly interfere with colorectal cancer. It can promote conditions to decrease exposure to risk factors, such as diet (primary prevention), and establish early diagnosis (secondary prevention).
Alarcão et al.³⁹ (2020)	Use of secondary data and collinear variables (schooling, income, and employment), which may impair the strength of the association, are limitations in this study.
Bonfim et al.³⁶ (2020)	Given the difference in coverage of the Mortality Information System throughout Brazil, the use of secondary data is a possible limitation in this study.

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[1] Correspondence: Maria Yury Ichihara Fundação Oswaldo Cruz Parque Tecnológico da Bahia Edif. Tecnocentro, sala 315 Rua Mundo, nº 121 41745-715 Salvador, BA, Brasil E-mail: my.coordenacao@gmail.com