Material deprivation, racial inequalities and mortality from female breast, prostate, and cervical neoplasm in the Brazilian adult population: an ecological study

Ana Cristina de Oliveira Costa Jackson Geraldo de Faria Júnior Guilherme Lopes de Oliveira Dandara de Oliveira Ramos Rômulo Paes-Sousa About the authors

Abstract

This article aims to identify the relationship between material deprivation and mortality from breast, cervical, and prostate neoplasms in the Brazilian adult population and the relationship between ethnicity/skin color and material deprivation. This cross-sectional ecological study calculated the mean mortality rate per 100,000 inhabitants, and deaths were standardized by age and gender and redistributed per to ill-defined causes, stratified by age group and ethnicity/skin color. We applied the Negative Binomial model, containing the interaction between ethnicity/skin color and the Brazilian Deprivation Index (IBP). We analyzed 85,903 deaths, and the most prevalent were those due to female breast neoplasms. The risk of death from cervical cancer was 8.5% higher for Black women than white women. In other places, mortality was higher among white people. For all causes, mortality increased with age. There was a significant interaction between ethnicity/skin color and IBP for all causes. Only deaths due to cervical neoplasms increased with higher IBP, while a decline was observed in other causes but was less significant among Black people. The IBP offers a multidimensional view of the socioeconomic conditions of the Brazilian population, allowing a better understanding of how social determinants operate on selected neoplasms.

Key words:
Material deprivation; Racism; Mortality; Neoplasms; Social inequalities

Introduction

The Global Cancer Observatory estimated approximately 10 million deaths from cancer worldwide in 2022. The escalating increase in cancer mortality is linked to higher life expectancy and aging of the population11 Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024; 74(3):229-263., besides changes in the prevalence and distribution of risk factors for diseases, mostly related to socioeconomic development22 Silva GA, Moura L, Curado MP, Gomes FS, Otero U, Rezende LF, Daumas RP, Guimarães RM, Meira KC, Leite IC, Valente JG, Moreira RI, Koifman R, Malta DC, Mello MS, Guedes TW, Boffetta P. The Fraction of Cancer Attributable to Ways of Life, Infections, Occupation, and Environmental Agents in Brazil in 2020. PloS One 2016; 11(12):e0148761..

Neoplasms are not distributed evenly among different social groups, and this is primarily due to unequal exposure to associated economic, social, and environmental determinants, and access to disease prevention, diagnosis, and treatment, which are modifiable factors33 Coughlin SS. Social determinants of breast cancer risk, stage, and survival. Breast Cancer Res Treat 2019; 177(3):537-548.,44 The Lancet Regional Health-Europe. Reduce cancer inequity and inequality to reduce cancer mortality. Lancet Reg Health Eur 2023; 25:100591.. Thus, neoplasms are now an issue on the agenda of policies to combat health inequalities44 The Lancet Regional Health-Europe. Reduce cancer inequity and inequality to reduce cancer mortality. Lancet Reg Health Eur 2023; 25:100591.,55 Vaccarella S, Georges D, Bray F, Ginsburg O, Charvat H, Martikainen P, Brønnum-Hansen H, Deboosere P, Bopp M, Leinsalu M, Artnik B, Lorenzoni V, De Vries E, Marmot M, Vineis P, Mackenbach J, Nusselder W. Socioeconomic inequalities in cancer mortality between and within countries in Europe: a population-based study. Lancet Reg Health Eur 2023; 25:100551., with the understanding that this is a matter of social justice and human rights and that it would also generate broad benefits in the economic sphere55 Vaccarella S, Georges D, Bray F, Ginsburg O, Charvat H, Martikainen P, Brønnum-Hansen H, Deboosere P, Bopp M, Leinsalu M, Artnik B, Lorenzoni V, De Vries E, Marmot M, Vineis P, Mackenbach J, Nusselder W. Socioeconomic inequalities in cancer mortality between and within countries in Europe: a population-based study. Lancet Reg Health Eur 2023; 25:100551.,66 Vineis P, Avendano-Pabon M, Barros H, Bartley M, Carmeli C, Carra L, Chadeau-Hyam M, Costa G, Delpierre C, D'Errico A, Fraga S, Giles G, Goldberg M, Kelly-Irving M, Kivimaki M, Lepage B, Lang T, Layte R, MacGuire F, Mackenbach JP, Marmot M, McCrory C, Milne RL, Muennig P, Nusselder W, Petrovic D, Polidoro S, Ricceri F, Robinson O, Stringhini S, Zins M. Special Report: The Biology of Inequalities in Health: The Lifepath Consortium. Front Public Health 2020; 8:118.. Non-modifiable factors such as race/ethnicity are also associated with the incidence and mortality from different types of neoplasms and generally intersect with modifiable social factors77 Lee MW, Vallejo A, Furey KB, Woll SM, Klar M, Roman LD, Wright JD, Matsuo K. Racial and ethnic differences in early death among gynecologic malignancy. Am J Obstet Gynecol 2024; 231(2):231.e1-231.e11.,88 Gupta A, Akinyemiju T. Trends in Cancer Mortality Disparities Between Black and White Individuals in the US, 2000-2020. JAMA Health Forum 2024; 5(1):e234617.. Studies on the relationship between race/ethnicity and mortality from neoplasms in the Brazilian population are still incipient and have sparked the interest of researchers on the subject. As a result, there is a greater understanding of this relationship and, subsequently, of the social adjustment mechanisms.

Using one-dimensional indicators such as education99 Ramos LFS, Sobrinho AR, Ribeiro LN, Martins-de-Barros AV, Maurício HA, Ferreira SJ, Carvalho MD. Racial disparity and prognosis in patients with mouth and oropharynx cancer in Brazil. Med Oral Patol Oral Cir Bucal 2022; 27(4):e392-e396. and income1010 Duarte DAP, Nogueira MC, Magalhães MC, Bustamante-Teixeira M. T. Iniquidade social e câncer de mama feminino: análise da mortalidade. Cad Saude Colet 2020; 28:465-476. is expected to understand the relationships between social inequalities and morbidity and mortality due to neoplasms. However, adopting multidimensional indicators such as the Human Development Index11 Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024; 74(3):229-263.,1111 Oliveira NPD, Cancela MC, Martins LFL, Souza DLB. Spatial distribution of advanced stage diagnosis and mortality of breast cancer: Socioeconomic and health service offer inequalities in Brazil. PLoS One 2021; 16(2):e0246333. has been in the rise to learn about the multiple factors producing these diseases. In the Brazilian context, we can study the correlations between morbidity and mortality indicators, sociodemographic indicators, and access to health goods and services, stratifying them per the social attributes of interest55 Vaccarella S, Georges D, Bray F, Ginsburg O, Charvat H, Martikainen P, Brønnum-Hansen H, Deboosere P, Bopp M, Leinsalu M, Artnik B, Lorenzoni V, De Vries E, Marmot M, Vineis P, Mackenbach J, Nusselder W. Socioeconomic inequalities in cancer mortality between and within countries in Europe: a population-based study. Lancet Reg Health Eur 2023; 25:100551..

The literature lacks studies that use multidimensional indicators to measure inequalities in mortality due to neoplasms1212 Costa ACO, Ramos DO, Sousa RP. Indicadores de desigualdades sociais associados à mortalidade por neoplasias nos adultos brasileiros: revisão de escopo. Cien Saude Colet 2024; 29(8):e19602022.. The demand for this analysis model has grown worldwide55 Vaccarella S, Georges D, Bray F, Ginsburg O, Charvat H, Martikainen P, Brønnum-Hansen H, Deboosere P, Bopp M, Leinsalu M, Artnik B, Lorenzoni V, De Vries E, Marmot M, Vineis P, Mackenbach J, Nusselder W. Socioeconomic inequalities in cancer mortality between and within countries in Europe: a population-based study. Lancet Reg Health Eur 2023; 25:100551.,1313 Batista HR, Mollo MLR. A questão da desigualdade multidimensional: discutindo a construção de um indicador. Rev Econ Contemp 2021; 25:e212516.. Some authors argue that multidimensional indicators would be more efficient in observing health inequalities than unidimensional indicators1313 Batista HR, Mollo MLR. A questão da desigualdade multidimensional: discutindo a construção de um indicador. Rev Econ Contemp 2021; 25:e212516.,1414 Howe LD, Galobardes B, Matijasevich A, Gordon D, Johnston D, Onwujekwe O, Patel R, Webb EA, Lawlor DA, Hargreaves JR. Measuring socio-economic position for epidemiological studies in low- and middle-income countries: a methods of measurement in epidemiology paper. Int J Epidemiol 2012; 41(3):871-886.. Brazil’s most widely used multidimensional indicators consider poverty measured through income and other dimensions essential to social dignity, such as access to health and education services and other dimensions of living conditions1515 Serra AS, Yalonetzky GI, Maia AG. Multidimensional Poverty in Brazil in the Early 21st Century: Evidence from the Demographic Census. Soc Indic Res Int Interdiscip J Qual Life Meas 2021; 154:79-114.,1616 Silva JJD, Bruno MAP, Silva DBDN. Multidimensional poverty in Brazil: analysis of the period 2004-2015. Braz J Polit Econ 2020; 40:138-160..

In 2019, in response to the demand for the use of multidimensional indicators based on data from the Population Census, which measures the social dimensions experienced by individuals or groups, the Center for Data Integration and Knowledge for Health (CIDACS) launched the Brazilian Deprivation Index (IBP). The IBP measures material deprivation, classifying it into levels of socioeconomic position in different Brazilian geographic areas, namely, census tracts, municipalities, states, macro-regions, and Brazil. The IBP measures social inequalities in health at different levels of spatial aggregation. It comprises the following variables: income, literacy, household characteristics, and access to adequate garbage collection, water, and sewage services1717 Allik M, Leyland AH, Dundas R. Small-area Deprivation Measure for Brazil: Data Documentation [Internet]. 2020 [cited 2024 jan 15]. Available from: https://researchdata.gla.ac.uk/980/.
https://researchdata.gla.ac.uk/980...
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This study specifically aimed to identify the relationship between material deprivation and mortality from female breast, cervical, and prostate cancer, which are common in the Brazilian population and whose outcomes are directly or indirectly associated with the most unfavorable socioeconomic conditions. Furthermore, we sought to verify the relationship between ethnicity/skin color and material deprivation, measured by the IBP.

Methods

A cross-sectional ecological study1818 Freire MCM, Pattussi MP. Tipos de estudos. In: Estrela C. Metodologia científica. Ciência, ensino e pesquisa. 3ª ed. Porto Alegre: Artes Médicas; 2018. p. 109-127. was conducted on mortality from female breast, cervical, and prostate cancer in 2009, 2010, and 2012 among the Brazilian adult population aged 20 years or older. Demographic and socioeconomic data by census tract were extracted from the 2010 Demographic Census database of the Brazilian Institute of Geography and Statistics (IBGE). Mortality data due to neoplasms were retrieved from the CIDACS1717 Allik M, Leyland AH, Dundas R. Small-area Deprivation Measure for Brazil: Data Documentation [Internet]. 2020 [cited 2024 jan 15]. Available from: https://researchdata.gla.ac.uk/980/.
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georeferencing database and classified under the International Classification of Diseases, 10th Revision (ICD-10). The study period was defined to cover the 2010 Demographic Census, the last Brazilian census with consolidated population microdata made available by the IBGE at the time of the analyses. We excluded 2011 from the study period due to its elevated percentage of data entry errors, which allowed the georeferencing of only 30.9% of deaths1919 Barbosa GCG, Ali MS, Araujo B, Reis S, Sena S, Ichihara MYT, Pescarini J, Fiaccone RL, Amorim LD, Pita R, Barreto ME, Smeeth L, Barreto ML. CIDACS-RL: a novel indexing search and scoring-based record linkage system for huge datasets with high accuracy and scalability. BMC Med Inform Decis Mak 2020; 20(1):289.. Regarding the three neoplasms, 84.03% of deaths recorded in the Mortality Information System (SIM) were georeferenced by CIDACS. This level of coverage is considered acceptable for ecological studies since Brazil has a 40% to 90% variation in the efficiency of georeferencing health data2020 Barcellos C, Tamalho WM, Gracie R, Magalhães MAFM, Fontes MP, Skaba D. Georreferenciamento de dados de saúde na escala submunicipal: algumas experiências no Brasil. Epidemiol Serv Saude 2008; 17(1):59-70. at sub-municipal scales.

The Bayesian method was applied to redistribute deaths from ill-defined causes in the census tracts2121 Cavalini LT, Ponce de Leon ACM. Correção de sub-registros de óbitos e proporção de internações por causas mal definidas. Rev Saude Publica 2007; 41:85-93.

22 Marshall RJ. Mapping Disease and Mortality Rates Using Empirical Bayes Estimators. J R Stat Soc Ser C Appl Stat 1991; 40:283-294.
-2323 Tu S. The Dirichlet-Multinomial and Dirichlet-Categorical models for Bayesian inference [Internet]. 2014 [cited 2024 jan 15]. Available from: https://stephentu.github.io/writeups/dirichlet-conjugate-prior.pdf.
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, considering as a reference the mortality pattern by gender (male and female), age group (‘20~34’, ‘35~49’, ‘50~69’, and ‘70+’) and ethnicity/skin color (white, composed of white and yellow individuals, and Black, composed of Black and brown individuals) of Brazil as a whole. We grouped white and yellow individuals2424 Coordenação de Controle de Doenças e Instituto de Saúde. Secretaria de Estado da Saúde de São Paulo. Causas de óbito segundo raça/cor e gênero no Estado de São Paulo. Rev Saude Publica 2005; 39:987-988.,2525 Batista LE, Rehder S. Saúde da População Negra no Estado de São Paulo. Suplemento 6 do Boletim Epidemiológico Paulista (Bepa). Vol. 3. São Paulo: Bepa; 2006. because the latter group corresponded to only 0.59% of the deaths analyzed and had similar mortality profiles. Bayesian redistribution is employed to minimize underreporting of deaths from a specific cause from the classification of deaths as deaths from ill-defined or very general causes (garbage codes) and to smooth out possible fluctuations resulting from the reduced number of cases, especially when it comes to rare outcomes or when the area analyzed does not have a considered sufficiently large sample size2626 Schmertmann CP, Gonzaga MR. Bayesian Estimation of Age-Specific Mortality and Life Expectancy for Small Areas with Defective Vital Records. Demography 2018; 55:1363-1388.. In this study, under Chapter II of ICD-10, the garbage codes redistributed were C76 (Malignant neoplasm of other and ill-defined sites), C79 (Secondary malignant neoplasm of other sites), C80 (Malignant neoplasm without specification of site) for all neoplasms, and C55 (Malignant neoplasm of uterus, part unspecified) for cervical neoplasms.

The material deprivation index used was the IBP, a single relative deprivation measure to analyze small areas (census tracts) with potential for aggregation to other geographic levels developed by CIDACS in partnership with the University of Glasgow-Scotland1717 Allik M, Leyland AH, Dundas R. Small-area Deprivation Measure for Brazil: Data Documentation [Internet]. 2020 [cited 2024 jan 15]. Available from: https://researchdata.gla.ac.uk/980/.
https://researchdata.gla.ac.uk/980...
. This index aims to verify and compare material deprivation calculated from variables extracted from the Population Census. The IBP was calculated by combining z-scores of the following variables: percentage of households with per capita income ≤1/2 minimum wage; percentage of illiterate people, aged 7 or over; and mean percentage of people with inadequate access to sewage, water, garbage collection, and lack of toilet and bathtub/shower. The z-score for each variable (x) was calculated using the formula z=(x-µ)/sd, with the mean (µ) and standard deviation (sd) for the individual indicators considering weights related to the population size of the census tracts. These z-scores were added and weighted into a single deprivation measure1717 Allik M, Leyland AH, Dundas R. Small-area Deprivation Measure for Brazil: Data Documentation [Internet]. 2020 [cited 2024 jan 15]. Available from: https://researchdata.gla.ac.uk/980/.
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The total number of deaths obtained after redistribution was aggregated into the 557 Brazilian microregions for each cause of mortality. Microregions are groups of contiguous municipalities defined as parts of the mesoregions that present specificities regarding space organization2727 Agência Nacional de Águas (ANA). Microrregiões. Catálogo de Metadados da ANA [Internet]. [acessado 2024 jan 15]. Disponível em: https://metadados.snirh.gov.br/geonetwork/srv/api/records/e6dd026c-afa7-4a7c-8904-abbb86662da5.
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, maintaining stratification by gender, ethnicity/skin color, and age group. For the IBP, the mean between the census tracts of the microregion was considered.

In order to descriptively explore the data, the mortality rate per 100,000 inhabitants was calculated for the three neoplasms in the 557 Brazilian microregions. Due to the large number of sampling units (combination of microregion, gender, ethnicity/skin color, and age group) with zero deaths due to the outcome, Poisson and Negative Binomial models with and without adjustment for excess zeros were tested to verify the analyses’ robustness and consistency2828 Lambert D. Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics 1992; 34:1-14.,2929 Hilbe JM. Negative binomial regression. 2ª ed. Cambridge: Cambridge University Press; 2011.. Fixed effects of gender, ethnicity/skin color, and age group were included, considering the categories previously defined in the formulation of the database. The IBP was included in the model to analyze its effect on the incidence rate of the outcomes. The inclusion of an interaction term between ethnicity/skin color and the IBP was also analyzed.

The AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), and RMSE (Root Mean Squared Error) metrics were employed to select the best model in each case3030 Chakrabarti A, Ghosh JK. AIC, BIC and Recent Advances in Model Selection. In: Bandyopadhyay PS, Forster MR, editors. Philosophy of Statistics. Vol. 7. Amsterdam: North-Holland; 2011. p. 583-605.. For all three metrics, the lower their value, the better the model fits the data. The analyses were performed using the R software3131 R: The R Project for Statistical Computing [Internet].[cited 2024 jan 15]. Available from: https://www.r-project.org/.
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with MASS (Modern Applied Statistics with S)3232 Ripley B, Venables B, Bates DM, Hornik K, Gebhardt A, Firth D. MASS: Support Functions and Datasets for Venables and Ripley's MASS [Internet]. 2024 [cited set 20]. Available from: https://cran.r-project.org/web/packages/MASS/MASS.pdf.
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packages to adjust the Negative Binomial models and pscl (Political Science Computational Laboratory)3333 Zeileis A, Kleiber C, Jackman S. Regression Models for Count Data in R. J Stat Softw 2008; 27:1-25. to adjust the zero-inflation models. The analyses are shown in tables with the results of the adjustment, containing the estimate of the coefficient of each effect included in the model (Estimate), the p-value of the significance test of the effect (P-value), the Relative Risk (RR), and its 95% confidence interval (95%CI).

Results

The model that best adjusted the data for all outcomes analyzed was the Negative Binomial (BN), without zero inflation and containing the interaction between ethnicity/skin color and the IBP. This model had the lowest values for the goodness-of-fit metrics considered (AIC, BIC, and RMSE). A total of 85,903 deaths were georeferenced for the three conditions investigated, with 60.7% of deaths recorded among women (for breast and cervical neoplasms). In the individualized observation of each neoplasm, mortality was observed for breast neoplasm (40.52%), followed by prostate (39.31%), and cervical (20.17%) neoplasms (Table 1).

Table 1
Descriptive characterization of the study population.

Cervical

A total of 17,332 deaths from cervical neoplasms were georeferenced during the study years, and 36.4% (n=203) of the microregions did not have records of deaths from these neoplasms. The results of the adjustment of the model to the data on mortality from cervical neoplasms are shown in Table 2. It was estimated that (i) women aged 35-49 years were 4.827 times more likely to be at risk of dying from cervical neoplasm than those aged 20-34 years; (ii) Black women’s risk of dying was 8.5% higher than for white women; (iii) the individual effect of IBP in the presence of the interaction was not significant with 10% significance, but it was relevant to the model since the interaction term of IBP with ethnicity/skin color is significant; (iv) the risk of death of Black women increases by 5.3% with every IBP unit increase.

Table 2
Negative binomial regression model adjusted to evaluate the mortality rate due to neoplasms of the cervical, female breast, and prostate, Brazil, 2009, 2010, and 2012.

Breast

Regarding mortality due to breast cancer, 34,805 deaths were georeferenced, and there were no deaths recorded due to this neoplasm in 34.9% (n=195) of the microregions. Table 2 shows the results of the model adjustment to the data on mortality due to breast cancer. It was estimated that (i) women aged 35-49 years are 9.132 times more likely to be at risk of death from breast cancer than women aged 20-34 years; (ii) the risk of death for Black women is 29.2% lower than for white women; (iii) the risk of death for white women decreases by 12.9% with every IBP unit increase; (iv) however, due to the interaction effect between ethnicity/skin color and IBP, the risk of death decreases by only 7.6% for Black women with every IBP unit increase; that is, the reduction is smaller than for white women.

Prostate

A total of 33,766 deaths from prostate neoplasms were georeferenced during the period, and 53.9% (n=298) of the microregions did not have records of prostate neoplasm deaths. Table 2 shows the results for mortality due to prostate neoplasms. It was estimated that (i) Men aged 35-49 are 18.330 times more likely to be at risk of dying from prostate cancer than men aged 20-34; (ii) Black men risk of dying is 6.4% lower than white men; (iii) The risk of dying among white men decreases by 8.1% for every IBP unit increase; (iv) However, due to the interaction effect between ethnicity/skin color and IBP, the risk of dying for Black men decreases by only 2.3% with every IBP unit increase.

Figure 1 shows the spatial distribution of the IBP and the incidence rates of mortality from cervical, breast, and prostate cancer per 100,000 inhabitants in the Brazilian microregions. In general, the North and Northeast regions of the country have a higher IBP; that is, these populations are exposed to more significant deprivation and are where mortality from cervical cancer is more significant. The same does not occur when it comes to mortality from breast and prostate cancer, which, in general, are more significant in the Midwest, Southeast, and South, which are less deprived.

Figure 1
Spatialization of the Brazilian Deprivation Index (IBP) (A) and the incidence rates of deaths due to cervical (B), female breast (C), and prostate (D) neoplasms per 100,000 inhabitants in the Brazilian microregions in 2009, 2010 and 2012.

Discussion

Mortality from the neoplasms studied does not follow a single distribution pattern, varying between age, ethnicity/skin color, and material deprivation. Understanding how it emerges in different scenarios is necessary to move toward reducing inequality in mortality.

Cervical neoplasms are still a relevant pathology despite the availability of effective technologies for its control. It is the fourth most common cause of incidence and mortality in women worldwide3434 Arbyn M, Weiderpass E, Bruni L, de Sanjosé S, Saraiya M, Ferlay J, Bray F. Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Glob Health 2020; 8(2):e191-e203.. This study observed that mortality from cervical neoplasm increases with age. This finding corroborates the study by Arbyn et al.3434 Arbyn M, Weiderpass E, Bruni L, de Sanjosé S, Saraiya M, Ferlay J, Bray F. Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Glob Health 2020; 8(2):e191-e203., which describes that mortality is more pronounced among women with a mean age of 56. Mortality was higher among Black women, a finding also evidenced by Góes et al.3535 Góes EF, Guimarães JMN, Almeida MDCC, Gabrielli L, Katikireddi SV, Campos AC, Matos SMA, Patrão AL, Oliveira Costa AC, Quaresma M, Leyland AH, Barreto ML, Dos-Santos-Silva I, Aquino EML. The intersection of race/ethnicity and socioeconomic status: inequalities in breast and cervical cancer mortality in 20,665,005 adult women from the 100 Million Brazilian Cohort. Ethn Health 2024; 29(1):46-61., who observed that mortality among Black and brown women was 18% and 27% higher, respectively, than white women. In this same study, the interaction between socioeconomic status and ethnicity/skin color evidenced that differences in mortality were more significant among women living in poorer household conditions; in the current research, the interaction between IBP and ethnicity/skin color showed that increased material deprivation is associated with a higher risk of death among Black women.

Breast cancer is the most common cancer among women worldwide11 Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024; 74(3):229-263.. Mortality from this condition increases with age. Camargo et al.3636 Camargo JDAS, Santos J, Simões TC, Carvalho JBL, Silva GWDS, Dantas ESO, Rodrigues WTDS, Freire FHMA, Meira KC. Mortality due to breast cancer in a region of high socioeconomic vulnerability in Brazil: Analysis of the effect of age-period and cohort. PLoS One 2021; 16(8):e0255935 (). affirm this is justified by the fact that this disease is associated with prolonged exposure to risk factors; that is, the time of exposure to its synthesizing factors increases with age. Another observation of this research that corroborates the literature is that mortality is affected by ethnicity and is rather pronounced among white women. In 2017, in São Paulo state, the mortality rate per 100,000 among white women was 16.46, higher than Black women, which was 9.573737 Marcelino AC, Gozzi B, Cardoso-Filho C, Machado H, Zeferino LC, Vale DB. Race disparities in mortality by breast cancer from 2000 to 2017 in São Paulo, Brazil: a population-based retrospective study. BMC Cancer 2021; 21(1):998.. This study reduces the risk of death among the most unfavorable economic strata. This finding was also described by Góes et al.3535 Góes EF, Guimarães JMN, Almeida MDCC, Gabrielli L, Katikireddi SV, Campos AC, Matos SMA, Patrão AL, Oliveira Costa AC, Quaresma M, Leyland AH, Barreto ML, Dos-Santos-Silva I, Aquino EML. The intersection of race/ethnicity and socioeconomic status: inequalities in breast and cervical cancer mortality in 20,665,005 adult women from the 100 Million Brazilian Cohort. Ethn Health 2024; 29(1):46-61., where women living in worse household conditions had a lower mortality risk than those with better conditions. Duarte et al.1010 Duarte DAP, Nogueira MC, Magalhães MC, Bustamante-Teixeira M. T. Iniquidade social e câncer de mama feminino: análise da mortalidade. Cad Saude Colet 2020; 28:465-476. also discuss the social effect on mortality from breast cancer; according to the study in the State of Minas Gerais in the Brazilian southeast, the highest mortality rates were found in the most developed and urbanized microregions of the state. The effect of social status on breast cancer is associated with exposure to risk factors. Women with better socioeconomic conditions tend to have greater access to hormonal contraceptives, nulliparity, or low parity, a long interval between menarche and the first pregnancy, higher age at first birth, and parity with lower breastfeeding rates, all of which are associated with the risk of developing neoplasms3838 Lyons S, Arcara J, Deardorff J, Gomez AM. Financial Strain and Contraceptive Use Among Women in the United States: Differential Effects by Age. Womens Health Issues 2019; 29(2):153-160.

39 Simoni MK, Mu L, Collins SC. Women's career priority is associated with attitudes towards family planning and ethical acceptance of reproductive technologies. Hum Reprod Oxf Engl 2017; 32(10):2069-2075.
-4040 Anderson KN, Schwab RB, Martinez ME. Reproductive risk factors and breast cancer subtypes: a review of the literature. Breast Cancer Res Treat 2014; 144(1):1-10..

Racial inequalities in reducing mortality due to increased material deprivation occurred for white women to a greater extent than for Black women, which can be explained by the fact that Black and white women tend to have access to diagnosis and treatment at different stages even in poor conditions, which directly impacts mortality. In 2019, a study by dos-Santos-Silva et al.4141 dos-Santos-Silva I, De Stavola BL, Renna NL Junior, Nogueira MC, Aquino EML, Bustamante-Teixeira MT, Silva GA. Ethnoracial and social trends in breast cancer staging at diagnosis in Brazil, 2001-14: a case only analysis. Lancet Glob Health 2019; 7(6):e observed that the diagnosis of advanced-stage breast cancer was more prevalent among Black and brown women with little or no formal education. This finding is corroborated by the study by Santos et al.4242 Santos TB, Borges AKDM, Ferreira JD, Meira KC, Souza MC, Guimarães RM, Jomar RT. Prevalência e fatores associados ao diagnóstico de câncer de mama em estágio avançado. Cien Saude Colet 2022; 27(2):471-482., which reaffirms that Black and brown women are most often diagnosed late. In 2019, a study by Cabral et al.4343 Cabral ALLV, Giatti L, Casale C, Cherchiglia ML. Social vulnerability and breast cancer: differentials in the interval between diagnosis and treatment of women with different sociodemographic profiles. Cien Saude Colet 2019; 24(2):613-622. showed that less educated Black women attended to by the public health system were 37 times more likely to have treatment started more than 90 days after diagnosis.

Prostate cancer is the second most common cancer among men in Brazil4444 Instituto Nacional de Câncer (INCA). Estimativa 2023: incidência de câncer no Brasil [Internet]. 2023 [acessado 2024 jan 15]. Disponível em: https://www.inca.gov.br/sites/ufu.sti.inca.local/files//media/document//estimativa-2023.pdf.
https://www.inca.gov.br/sites/ufu.sti.in...
. Increasing age is related to higher mortality, which its slow development4545 Evangelista FM, Melanda FN, Modesto VC, Soares MR, Neves MAB, Souza BSN, Sousa NFS, Galvão ND, Andrade ACS. Incidência, mortalidade e sobrevida do câncer de próstata em dois municípios com alto índice de desenvolvimento humano de Mato Grosso, Brasil. Rev Bras Epidemiol 2022; 25(Supl. 1):e220016.,4646 Lima CA, Silva BEB, Hora EC, Lima MS, Brito EAC, Santos MO, Silva AM, Nunes MAP, Brito HLF, Lima MMM. Trends in prostate cancer incidence and mortality to monitor control policies in a northeastern Brazilian state. PLoS One 2021; 16(3):e0249009. may explain. Another important fact is the impact of race on mortality. This study showed that white men are more likely to be at risk of dying than Black men, but this finding is not consensual in the literature. In 2023, Zeng et al.4747 Zeng H, Xu M, Xie Y, Nawrocki S, Morze J, Ran X, Shan T, Xia C, Wang Y, Lu L, Yu XQ, Azeredo CM, Ji JS, Yuan X, Curi-Quinto K, Liu Y, Liu B, Wang T, Ping H, Giovannucci EL. Racial/ethnic disparities in the cause of death among patients with prostate cancer in the United States from 1995 to 2019: a population-based retrospective cohort study. EClinicalMedicine 2023; 62:102138. evaluated the racial/ethnic disparities reported in the United States, concluding that the mortality rate was higher among Black people. In Brazil, a study by Oliveira et al.4848 Oliveira MM, Malta DC, Guauche H, Moura L, Silva GA. Estimativa de pessoas com diagnóstico de câncer no Brasil: dados da Pesquisa Nacional de Saúde, 2013. Rev Bras Epidemiol 2015; 18(Supl. 2):146-157. with data from the 2013 National Health Survey revealed that the diagnosis of neoplasms was higher among self-declared white people, which may justify the finding of this study since incidence affects mortality. People’s deprivation condition also impacts mortality, in which it was inversely proportional to mortality. This finding does not corroborate the literature, where populations with better socioeconomic conditions have lower mortality4949 Ribeiro AG, Ferlay J, Vaccarella S, Latorre MDRDO, Fregnani JHTG, Bray F. Cancer inequalities in incidence and mortality in the State of São Paulo, Brazil 2001-17. Cancer Med 2023; 12(15):16615-16625.,5050 Su S-Y. Geographical variations of socioeconomic status and prostate cancer mortality in Taiwan. Cancer Causes Control 2021; 32(3):203-210.. However, this finding may be justified by the fact that in Brazil, people living in areas with worse socioeconomic conditions have less access to diagnosis and timely diagnosis5151 Paulista JS, Assunção PG, Lima FLT. Acessibilidade da População Negra ao Cuidado Oncológico no Brasil: Revisão Integrativa. Rev Bras Cancerol 2019; 65:e-06453. and have underreported cases, with poorer data quality and a high proportion of deaths recorded as ill-defined causes, which reflect unsatisfactory care conditions and hinders the identification of the real cause of death5252 Costa ACO, Ferreira BH, Souza MR, Costa Filho AM, Souza AA. Análise da qualidade da informação sobre óbitos por neoplasias no Brasil, entre 2009 e 2019. Rev Bras Epidemiol 2022; 25:e220022.. An interaction between material deprivation and ethnicity/skin color was observed in this study; mortality was reduced with higher deprivation, but this reduction was less significant among the Black population. In other words, even in similar conditions of deprivation, Black and white men tend to have access to diagnosis and treatment at different stages. Zacchi et al.5353 Zacchi SR, Amorim MHC, Souza MAC, Miotto MHMB, Zandonade E. Associação de variáveis sociodemográficas e clínicas com o estadiamento inicial em homens com câncer de próstata. Cad Saude Colet 2014; 22:93-100. affirm that non-white men were more likely to be diagnosed at more advanced stages than white men. Corroborating this, Souza et al.5454 Souza ABC, Guedes HG, Oliveira VC, de Araújo FA, Ramos CC, Medeiros KC, Araújo Jr. RF. High incidence of prostate cancer metastasis in Afro-Brazilian men with low educational levels: a retrospective observational study. BMC Public Health 2013; 13:537. describe that Black men and less educated men (<8 years) were more likely to have advanced disease at the time of diagnosis.

Different mechanisms of racial inequalities affect mortality from neoplasms. The finding of this study deserves special attention, where, under similar conditions of deprivation, the reduction in mortality among Black people due to female breast and prostate cancer is less pronounced than the reduction among white people. This fact points to Black people’s social disadvantage in Brazil because of structural racism, which affects the health-disease process5555 Sung H, Nisotel L, Sedeta E, Islami F, Jemal A. Racial and Ethnic Disparities in Survival Among People with Second Primary Cancer in the US. JAMA Netw Open 2023; 6:e2327429.. Racism is a socially organized system that causes avoidable and unjust inequalities of power, resources, capabilities, and opportunities between racial groups5656 Paradies Y, Ben J, Denson N, Elias A, Priest N, Pieterse A, Gupta A, Kelaher M, Gee G. Racism as a Determinant of Health: A Systematic Review and Meta-Analysis. PLoS One 2015; 10(9):e0138511.. Racism submits individuals to disparate exposure to risk factors and systemic barriers to screening and treatment, partly due to underlying socioeconomic inequalities3535 Góes EF, Guimarães JMN, Almeida MDCC, Gabrielli L, Katikireddi SV, Campos AC, Matos SMA, Patrão AL, Oliveira Costa AC, Quaresma M, Leyland AH, Barreto ML, Dos-Santos-Silva I, Aquino EML. The intersection of race/ethnicity and socioeconomic status: inequalities in breast and cervical cancer mortality in 20,665,005 adult women from the 100 Million Brazilian Cohort. Ethn Health 2024; 29(1):46-61.,5555 Sung H, Nisotel L, Sedeta E, Islami F, Jemal A. Racial and Ethnic Disparities in Survival Among People with Second Primary Cancer in the US. JAMA Netw Open 2023; 6:e2327429.

56 Paradies Y, Ben J, Denson N, Elias A, Priest N, Pieterse A, Gupta A, Kelaher M, Gee G. Racism as a Determinant of Health: A Systematic Review and Meta-Analysis. PLoS One 2015; 10(9):e0138511.

57 Williams DR, Lawrence JA, Davis BA, Vu C. Understanding how discrimination can affect health. Health Serv Res 2019; 54(Supl. 2):1374-1388.
-5858 Bailey ZD, Krieger N, Agénor M, Graves J, Linos N, Bassett MT. Structural racism and health inequities in the USA: evidence and interventions. Lancet 2017; 389(10077):1453-1463..

Regarding the effect of deprivation on mortality from neoplasms, the differences found in this study can be justified by the varying socioeconomic measures adopted to measure inequalities, with studies using primarily one-dimensional measures, which cannot fully describe the complex disparities between diverse groups1313 Batista HR, Mollo MLR. A questão da desigualdade multidimensional: discutindo a construção de um indicador. Rev Econ Contemp 2021; 25:e212516.. Employing the IBP aimed to unify the different dimensions of inequalities and offer a more realistic response regarding the health conditions of the Brazilian population.

The current age profile is marked by population aging, which makes individuals more susceptible to illness and mortality from neoplasms, directly impacting the health budget and encouraging the reorganization of spending and policies to address these conditions. In 2022, Brazil invested approximately R$ 3.9 billion in cancer treatment5959 Observatório de Oncologia. Quanto custa tratar um paciente com câncer no SUS? [Internet]. 2024 [acessado 2024 set 15]. Disponível em: https://observatoriodeoncologia.com.br/estudos/tratamento-em-oncologia/2024/custo-do-cancer-no-sus/#:~:text=De%20acordo%20com%20o%20levantamento,15%20para%20R%24758%2C93.
https://observatoriodeoncologia.com.br/e...
, but it is known that financial investment in health does not guarantee overall favorable results6060 Aggarwal A, Ginsburg O, Fojo T. Cancer economics, policy and politics: What informs the debate? Perspectives from the EU, Canada and US. J Cancer Policy 2014; 2:1-11.. This fact is evident in the significant inequalities in the incidence of specific neoplasms and mortality rates affecting different social strata. When designing and proposing health policies to reduce inequalities, one should consider the social and environmental contexts in which individuals are “born, grow, work, live and age”6161 Donkin A, Goldblatt P, Allen J, Nathanson V, Marmot M. Global action on the social determinants of health. BMJ Glob Health 2018; 3:e000603. and have measures, such as the IBP, that broadly reflect inequalities. There is a growing need to disseminate through policies the timely access to specialized services and preventive measures and reduce exposure to risk factors, such as high body mass index, low consumption of fruits and vegetables, lack of physical activity and alcohol and tobacco use, which are currently responsible for one-third of mortality from neoplasms worldwide6262 World Health Organization (WHO). Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013-2020. Geneva: WHO; 2013.,6363 Instituto Nacional do Câncer (INCA). Dieta, nutrição, atividade física e câncer: uma perspectiva global: um resumo do terceiro relatório de especialistas com uma perspectiva brasileira. Rio de Janeiro: INCA; 2018..

This study highlights the use of a composite measure validated in Brazil, developed to fill the gap in health research resulting from the lack of a tool that could be applied at different geographic levels but for the entire national territory. The IBP is an estimate, and as such, it has some degree of uncertainty about the exact level of deprivation for all areas caused by different factors, such as small population size and differences in deprivation estimated by the indicators adopted in the measure. The IBP gains in health research are that uncertainty is sufficiently small to place the area reliably in a deprivation category1717 Allik M, Leyland AH, Dundas R. Small-area Deprivation Measure for Brazil: Data Documentation [Internet]. 2020 [cited 2024 jan 15]. Available from: https://researchdata.gla.ac.uk/980/.
https://researchdata.gla.ac.uk/980...
for most census tracts (95.5%) and population (97.5%).

A limitation of this study is that it lacks essential measures in assessing the effect of deprivation on mortality from neoplasms, such as geographic accessibility to specialized services, measurement of the time between diagnosis and start of treatment, and difference in the diagnosis stage in different social contexts. Furthermore, no other indicator or index for assessing socioeconomic position was used with the IBP, as recommended for research, as no single measure can answer all questions1414 Howe LD, Galobardes B, Matijasevich A, Gordon D, Johnston D, Onwujekwe O, Patel R, Webb EA, Lawlor DA, Hargreaves JR. Measuring socio-economic position for epidemiological studies in low- and middle-income countries: a methods of measurement in epidemiology paper. Int J Epidemiol 2012; 41(3):871-886.. Data temporality is another limiting factor, mitigated by using consolidated census data. This methodology should be replicated using data from the last demographic census (2022).

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Publication Dates

  • Publication in this collection
    27 Jan 2025
  • Date of issue
    Jan 2025

History

  • Received
    11 Feb 2024
  • Accepted
    10 Aug 2024
  • Published
    12 Aug 2024
ABRASCO - Associação Brasileira de Saúde Coletiva Rio de Janeiro - RJ - Brazil
E-mail: revscol@fiocruz.br