Race and sex disparities in the prevalence of multimorbidity due to chronic diseases: cross-sectional study, BrazilAbstract
Objective: To examine disparities according to race/skin color and sex, and their intersectionality in the prevalence of multimorbidity among Brazilian adults.
Methods: This was a cross-sectional study with adults participating in the 2019 National Health Survey. The database and dictionary of variables are available on the Brazilian Institute of Geography and Statistics website. Multimorbidity (yes/no) was defined as the simultaneous presence of two or more chronic conditions. Poisson regression with robust variance was used to investigate associations between race/skin color (White, Brown and Black), sex (male and female), race/skin color-sex intersectionality (White males, Brown males, Black males, White females, Brown females and Black females), and multimorbidity. Prevalence ratios (PR) and their 95% confidence intervals (95%CI) were obtained. The analyses were adjusted for age and schooling.
Results: Among the 57,229 participants, prevalence of multimorbidity was 24.3%. Brown people (PR 0.92; 95%CI 0.87; 0.98) had lower prevalence compared to White people. Females showed a higher prevalence of multimorbidity compared to males (PR 1.62; 95%CI 1.53; 1.71). The intersectionality analysis indicated higher prevalence of multimorbidity among White women (PR 1.58; 95%CI 1.46; 1.72), Brown women (PR 1.48; 95%CI 1.36; 1.60) and Black women (PR 1.55; 95%CI 1.39; 1.72), compared to White men.
Conclusion: The study identified lower prevalence of multimorbidity among Brown people, and higher prevalence among females. Intersectionality revealed that only females, regardless of race/skin color, had higher prevalence compared to White males. The findings highlighted disparities according to race/skin color and sex in the prevalence of multimorbidity among Brazilian adults.
Keywords:
Gender Analysis in Health; Race Factors; Multimorbidity; Social Determinants of Health; Cross-Sectional Studies
Resumo
Objetivo: Examinar disparidades por raça/cor da pele, sexo e sua interseccionalidade na prevalência de multimorbidade entre adultos brasileiros.
Métodos: Estudo transversal com adultos participantes da Pesquisa Nacional de Saúde, 2019. A base de dados e o dicionário de variáveis estão disponíveis no site do Instituto Brasileiro de Geografia e Estatística. A multimorbidade (sim/não) foi considerada como a presença simultânea de duas ou mais condições crônicas. Para investigar as associações entre raça/cor da pele (brancos, pardos e pretos), sexo (homens e mulheres), interseccionalidade raça/cor da pele-sexo (homens brancos, homens pardos, homens pretos, mulheres brancas, mulheres pardas e mulheres pretas) e multimorbidade foi utilizada regressão de Poisson com variância robusta. Obtiveram-se razões de prevalência (RP) e seus intervalos de confiança de 95% (IC95%). As análises foram ajustadas para idade e escolaridade.
Resultados: Dentre os 57.229 participantes, a prevalência de multimorbidade foi 24,3%. Pessoas pardas (RP 0,92; IC95% 0,87; 0,98) tiveram menores prevalências em comparação às brancas. Mulheres mostraram maior prevalência de multimorbidade em comparação aos homens (RP 1,62; IC95% 1,53; 1,71). A análise interseccional apontou maiores prevalências de multimorbidade entre mulheres brancas (RP 1,58; IC95% 1,46; 1,72), pardas (RP 1,48; IC95% 1,36; 1,60) e pretas (RP 1,55; IC95% 1,39; 1,72), comparadas aos homens brancos.
Conclusão: O estudo identificou menor prevalência de multimorbidade entre pardos, e maior entre mulheres. A interseccionalidade revelou que apenas mulheres, independentemente da raça/cor da pele, apresentaram maior prevalência comparadas aos homens brancos. Os achados evidenciaram disparidades por raça/cor da pele e sexo na prevalência de multimorbidade em adultos brasileiros.
Palavras-chave:
Análise de Gênero na Saúde; Fatores Raciais; Multimorbidade; Determinantes Sociais da Saúde; Estudos Transversais.
Resumen
Objetivo: Examinar las disparidades por raza/color de piel, sexo y su interseccionalidad en la prevalencia de multimorbilidad en adultos brasileños.
Métodos: Estudio transversal con adultos que participaron en la Encuesta Nacional de Salud de 2019. La base de datos y el diccionario de variables están disponibles en el sitio web del Instituto Brasileño de Geografía y Estadística. La multimorbilidad (sí/no) se definió como la presencia simultánea de dos o más enfermedades crónicas. Para investigar las asociaciones entre raza/color de piel (blanco, pardo y negro), sexo (hombres y mujeres), interseccionalidad raza/color de piel-sexo (hombres blancos, hombres pardos, hombres negros, mujeres blancas, mujeres pardas y mujeres negras) y multimorbilidad, se utilizó una regresión de Poisson con varianza robusta. Se obtuvieron las razones de prevalencia (RP) y sus intervalos de confianza del 95% (IC del 95%). Los análisis se ajustaron por edad y educación.
Resultados: Entre los 57.229 participantes, la prevalencia de multimorbilidad fue del 24,3%. Las personas pardas (RP 0,92; IC95% 0,87; 0,98) tuvieron una prevalencia menor en comparación con las personas blancas. Las mujeres mostraron una prevalencia mayor de multimorbilidad en comparación con los hombres (RP 1,62; IC95% 1,53; 1,71). El análisis interseccional indicó una prevalencia mayor de multimorbilidad entre las mujeres blancas (RP 1,58; IC95% 1,46; 1,72), mujeres pardas (RP 1,48; IC95% 1,36; 1,60) y mujeres negras (RP 1,55; IC95% 1,39; 1,72), en comparación con los hombres blancos.
Conclusión: El estudio identificó una prevalencia menor de multimorbilidad entre las personas de piel morena y una prevalencia mayor entre las mujeres. La interseccionalidad reveló que solo las mujeres, independientemente de su raza o color de piel, presentaron una prevalencia mayor que los hombres blancos. Los hallazgos destacaron las disparidades por raza, color de piel y sexo en la prevalencia de multimorbilidad en adultos brasileños.
Palabras clave:
Análisis de Género en Salud; Factores Raciales; Multimorbidad; Determinantes Sociales de la Salud; Estudios Transversales.
This research respected ethical principles, having obtained the following approval data:
Research ethics committee: National Research Ethics Committee
Opinion number: 3,529,376
Approval date: 23/8/2019
Certificate of submission for ethical appraisal: 11713319.7.0000.0008
Informed consent record: Obtained from all participants prior to data collection.
Introduction
Chronic noncommunicable diseases (NCDs), such as cardiovascular disease, diabetes, cancer, chronic respiratory disease and mental disorders, accounted for nearly three-quarters of global deaths in 2019, corresponding to 41 million people 1. Each year, approximately 17 million people die from NCDs worldwide, and 86.0% of these deaths, more than 14 million, occur in low- and middle-income countries 1. In Brazil, NCDs accounted for 54.7% of deaths in the same year 2. Global prevalence of multimorbidity, defined as the simultaneous presence of two or more chronic conditions or diseases, is 37.2% among adults 3, while in Brazil it is estimated at 24.1% 4. Compared to the presence of a single condition, multimorbidity poses a challenge to health systems because it demands more complex care, increases the use and costs of services, as well as increasing the risk of complications, disabilities and premature death 4,5.
A debate exists as to the role of social determinants, such as sex, race/skin color, schooling and income, in the occurrence of chronic diseases, which can impact living conditions and, consequently, illness 6,7. Sexism is a determining factor in social and health inequalities present throughout women’s lives8. In addition to commonly being responsible for family health care, women seek health services more frequently 9,10, which has contributed to higher frequency of clinical diagnosis and, therefore, higher multimorbidity incidence 6. A cross-sectional analysis of more than 60,000 adults who took part in the 2013 National Health Survey identified higher multimorbidity prevalence among women compared to men, including at younger ages 11, highlighting their greater vulnerability to NCDs.
A greater accumulation of multiple diseases can also be observed among people of Black and Brown/skin color, whose health conditions are impacted by the discrimination and oppression they experience 12,13. Black and Brown/skin color is associated with reduced access to health services, such as consultations and examinations 14,15, as well as a higher frequency of unfavorable health behaviors and conditions, such as excess weight, smoking and alcohol abuse, factors known to be associated with the occurrence of NCDs 10,16,17.
The theory of intersectionality deepens this discussion by considering that, in societies, relationships involving race and gender do not occur indiscriminately; on the contrary, they overlap and function in a unified manner, affecting different areas of life and health 18. Thus, it must be considered that on their own these social determinants are capable of producing effects, but that, together, their effects are potentialized 15,17,18.
Although studies have been conducted on race/skin color and sex in isolation in the occurrence of multimorbidity among Brazilians 4,9,11, no population-based analysis has considered intersectionality characteristics between these exposures in order to investigate interaction between race/skin color and sex-used as a proxy for gender. Given that Brazil is a country with a population marked by social inequalities due to gender and race/skin color, and given the high prevalence of chronic noncommunicable diseases in the Brazilian population 2, understanding this relationship is crucial.
Therefore, using data from the largest population survey conducted with adults in Brazil, this study aims to examine disparities by race/skin color and by sex, and their intersectionality in the prevalence of multimorbidity among Brazilian adults.
Methods
Study design
This was a cross-sectional study using population-based data.
Setting
The second edition of the National Health Survey (Pesquisa Nacional de Saúde - PNS) was conducted in 2019 by the Brazilian Institute of Geography and Statistics (IBGE), the Ministry of Health and the Oswaldo Cruz Foundation. The target audience was individuals aged 15 or older living in permanent private households, excluding special census tracts. The PNS sample originated from a master sample composed of a set of area units selected from a registry (primary units). In order to address the complexity of the sampling design, sampling weights were assigned to the selected households and residents for better data analysis. The final weighting applied is a product of the inverse of the selection probability expressions for each stage of the sampling plan, which includes correction for non-respondents and adjustments for the total population 19,20.
Data source and measurement
The PNS collected information on the health status and lifestyles of the Brazilian population, as well as access to and use of health services, working conditions and violence. In all, the 2019 edition contained 26 question modules.
Data for the 2019 PNS were collected from 94,114 households, achieving a response rate of 96.5%. Data collection took place between August 2019 and March 2020. The PNS datasets used in this study are freely available on the website of the Brazilian Institute of Geography and Statistics 21 and were accessed by us on June 1, 2023.
Participants
For the purposes of the analysis performed in this study, we included adult participants aged 18 to 59 years and those who provided sociodemographic and chronic disease information. Participants who self-reported their race/skin color as Indigenous Brazilians and Asian descendants were not included, as they constituted a very small sample, which would significantly reduce the statistical power for analysis of this category 22.
Variables
Response variable
Multimorbidity (no/yes) was defined by a self-reported history of two or more of the following medical diagnoses: hypertension, diabetes, hypercholesterolemia, stroke, cardiovascular disease, chronic kidney disease, arthritis/rheumatism, asthma, chronic back problem, work-related musculoskeletal disorder, chronic obstructive pulmonary disease, depression, mental illness and cancer.
In order to create this variable, all affirmative responses to questions such as “Has a doctor ever diagnosed you as having...?” (module Q - chronic diseases) were considered. The response options for the question were “yes” and “no”.
Exposure variables
Three exposure variables were considered. The first was self-reported race/skin color, obtained through the “color or race” question, with the White, Brown and Black categories being considered in the analyses. The second was sex, considering man and woman, used as a proxy for gender. Information on sex and race/skin color was obtained from module C - general characteristics of household residents.
The third variable, intersectionality, was created from the combination of the sex and race/skin color variables categories, with the aim of analyzing how these social attributes interact with each other 18. Thus, the investigation resulted in six categories: White males (reference), Brown males, Black males, White females, Brown females and Black females.
Adjustment variables
The following adjustment variables were considered: age in years (continuous and in age groups: 18-24, 25-34, 35-44, 45-54 and 55-59), since aging is associated with biological changes that favor the appearance of chronic diseases (5,11); and schooling (complete higher education - 1st degree or above, complete high school education, complete elementary education, and incomplete elementary education), as it is commonly used as a proxy for socioeconomic status in adulthood (23).
Statistical methods
The descriptive analysis was stratified by sex and race/skin color (Table 1). Sociodemographic characteristics were presented using frequencies, along with their 95% confidence intervals (95%CI). Prevalence rates and their respective 95%CI were calculated for chronic conditions. The numerator for each prevalence rate was those who answered “yes” to having a given disease/chronic condition in relation to the total study population, with the result of this ratio being multiplied by 100. Next, the prevalence of multimorbidity was calculated and expressed as a percentage, using all individuals who had two or more chronic conditions as the numerator and the total study population as the denominator.
For the bivariate analysis, differences in multimorbidity prevalence according to the intersectional characteristics of race/skin color and sex were tested using Pearson’s chi-square test (Figure 1). In the multivariate analysis, Poisson regression was used to investigate association between race/skin color, sex, and race/skin color and sex intersectionality, and the presence of multimorbidity (Table 2). Our hypothesis was that females compared to males; Brown and Black males compared to White males; Brown and Black males and White, Brown and Black females compared to White males, have higher multimorbidity prevalence rates.
Three models were estimated in order to measure these relationships: a crude model between each exposure and response variable, namely race/skin color and multimorbidity, sex and multimorbidity, and race/skin color and sex intersectionality and multimorbidity (model 0). Then, adjustments for continuous age (model 1) and, finally, for schooling (model 2) were included. We provided the prevalence ratio (PR) and 95%CI for each model. A 5% significance level (p-value <0.05) was adopted in all statistical tests. All statistical analyses were performed using Stata 14.0 (Stata Corporation, College Station, United States) using the survey module, which considers the effects of complex sampling.
Results
The study population consisted of 57,229 participants who provided information on their sex classification, and 57,225 on race/skin color, comprising different sample sizes. The description of the study population is presented in Table 1. Multimorbidity prevalence in the total population was 24.3% (95%CI 23.6; 24.9). Among those with multimorbidity, there was a higher proportion of women (66.0%; 95%CI 64.6; 67.3), those aged 45 to 54 years (35.0%; 95%CI 33.5; 36.4), White (45.9%; 95%CI 44.3; 47.5), and those with incomplete elementary education (34.5%; 95%CI 32.9; 36.0), compared to the total population.
Study population characteristics, proportions and 95% confidence intervals (95%CI). Brazil, 2019, n= 57,229 (exposure by sex) and n=57,225 (exposure by race/skin color)
Figure 1 describes the bivariate analysis that estimated the differences in multimorbidity prevalence according to the race/skin color and sex intersectionality characteristics. White females (31,01%; 95%CI 29.5; 32.5), Brown females (27,77%; 95%CI 26.6; 28.9) and Black females (29,19%; 95%CI 26.8; 31.7) had higher multimorbidity prevalence rates when compared to White males (19,46%; 95%CI 18.1; 20.9), Brown males (17,29%; 95%CI 16.1; 18.6) and Black males (16.67%; 95%CI 14.6; 18.9) (p-value<0.05). Among males, no differences were observed in multimorbidity prevalence between race/skin color strata (p-value>0.05). White females (31.0%; 95%CI 29.5; 32.5), in turn, had higher multimorbidity prevalence compared to Brown females (27.8%; 95%CI 26.6; 28.9) (p-value<0.05).
Multimorbidity prevalence and 95% confidence intervals (95%CI), by sex and self-reported race/skin color. Brazil, 2019 (n=57,225)
Crude and adjusted multimorbidity prevalence ratios (PR) and their 95% confidence intervals, by self-reported race/skin color, sex and intersectionality characteristics. Brazil, 2019 (n=57,225)
Table 2 presents the multivariate analysis between each form of exposure and outcome. Regarding race/skin color, Brown and Black people had lower multimorbidity prevalence rates when compared to White people in the crude model (PR Brown 0.89 95%CI 0.84; 0.94; PR Black 0.90; 85%CI 0.83; 0.98). When including adjustment for age and schooling (model 2), only Brown people remained statistically associated with the presence of multimorbidity (PR 0.92; 95%CI 0.87; 0.98). Regarding sex, females had higher multimorbidity prevalence compared to males in the crude model (PR 1.61; 95%CI 1.53; 1.70). This association remained statistically significant, even after adjustments for age and schooling (PR 1.62; 95%CI 1.53; 1.71).
Considering the intersectional analysis, Brown males (PR 0.88; 95%CI 0.79; 0.98) and Black males (PR 0.85; 95%CI 0.73; 0.99) had lower multimorbidity prevalence rates compared to White males. White females (PR 1.59; 95%CI 1.46; 1.73), Brown females (PR 1.42; 95%CI 1.31; 1.55), and Black females (PR 1.50; 95%CI 1.34; 1.67) had higher prevalence rates compared to White males. After adjusting for age and schooling, Brown and Black males did not remain statically associated with occurrence of multimorbidity. Among females, White (PR 1.58; 95%CI 1.46; 1.72), Brown (PR 1.48; 95%CI 1.36; 1.60) and Black (PR 1.55; 95%CI 1.39; 1.72) females had higher prevalence rates compared to White males (Table 2).
Supplemental Table 1 describes the prevalence of the diseases and chronic conditions assessed. In the total population, the most prevalent conditions were chronic back problems (19.3%), hypertension (18.3%) and hypercholesterolemia (12.3%). When analyzing according to intersectionality, the most prevalent conditions were arthritis/rheumatism, chronic back problems, depression and mental illness in White, Brown and Black females when compared to their male counterparts.
Discussion
This study found that women tend to have higher multimorbidity prevalence rates compared to men. Among Brown individuals, multimorbidity prevalence was lower than among White individuals. When analyzing the race/skin color and sex intersectionality characteristics, higher multimorbidity prevalence was found among White, Black and Brown females compared to White males. Prevalence among Brown and Black males was similar to that found among White males.
Some limitations of this study need to be highlighted. First, self-reported data on morbidities may involve information bias. Second, it had a cross-sectional design, subject to biases peculiar to this design, although the exposure variables precede the outcome temporally. There may have been survival bias, especially in White individuals and females, which may have influenced the results. Third, the sex variable was used as a proxy for gender, which implies limitations, since sex constitutes a binary classification that does not encompass gender identity diversity. The need to improve measurement of gender in future population surveys such as the National Health Survey is highlighted. Finally, due to the cross-sectional design, a conservative adjustment was adopted, using age as a confounder and schooling as a possible mediator. Estimates of the magnitudes of associations independent of schooling-a proxy for socioeconomic status-highlight the direct mechanism of exposure. Other factors, such as risk behaviors, were not included because they are possible mediators and have bidirectional relationships with the outcome.
In this study, women had higher multimorbidity prevalence rates than men, possibly because they seek health services more frequently, resulting in more diagnoses and self-reported chronic diseases 4,10,11. A study using data from the National Survey on Access, Use and Promotion of Rational Use of Medication in Brazil 9, involving more than 23,000 adults (52.8% females), found that multimorbidity prevalence was two times higher among females than among males (14.5% versus 6.8%). Similarly, a study based on the 2019 National Health Survey identified a higher proportion of medical consultations in the 12 months prior to the interview among females (82.3%) compared to males (69.4%) 10. Socially determined gender roles, such as the female role of family caregiver, contribute to greater health care 10,24, while gender inequalities, such as double work shifts, employment in more precarious jobs, lower pay and greater exposure to violence, generate overload and negative impacts on mental health 8,10,24. In addition, prevalence of depression was approximately three times higher among females than among males in this study (Supplementary Table 1), highlighting gender-related stress as an important factor in illness. Furthermore, previous evidence suggests that this effect may be exacerbated when considering the racial dimension within gender issues 8,10.
Gender, as a social construct, shapes behaviors and exposures that impact health 8,24. Men are more exposed to occupational and social risks, such as traffic accidents and violence 10,24, and have higher chronic disease mortality rates. In 2019, 56.1% of premature deaths due to chronic noncommunicable diseases (NCDs) in Brazil occurred among men 2. Despite this increased risk, the cultural construction of masculinity discourages men from seeking medical care, which is limited to serious situations, as shown by the 2019 National Health Survey. Men sought more health care related to accidents, injuries or fractures, with higher prevalence among Brown and Black men (8.7%) than among White men (6.4%) 10.
The findings of this study emphasize gender inequalities, showing that females, regardless of race/skin color, have higher prevalence of multimorbidity and self-reported diseases compared to males. Racial disparities are also observed within a given gender itself: prevalence of mental illness, depression and cancer was higher among White females, compared to Black and Brown females. These diseases require greater access to health services for diagnosis, and are more common among White females (Supplementary Table 1) 10,16,17. The literature indicates that multimorbidity is directly related to the use of these services 4,9,10,14, access to which is lower among men and Black and Brown individuals, who face barriers to medical and dental appointments and obtaining medication 10,16,17. Data from the Chronic Disease Risk and Protective Factors Surveillance Telephone Survey System (Sistema de Vigilância de Fatores de Risco e Proteção para Doenças Crônicas por Inquérito Telefônico - VIGITEL) emphasize this construct by indicating lower rates of cytological exams and mammograms among Black females 16, in addition to cervical cancer being two times more prevalent in Black females compared to White females, this being a disease related to access to health services 17. Data from the 2019 PNS reveal a higher frequency of medical consultations in the last 12 months among White females (86.4%) than among Black females (82.8%), as well as among White males (75.8%) compared to Black or Brown males (68.3%) 10.
One of the hypotheses investigated was that multimorbidity prevalence would be higher among Brown and Black individuals. Data from Adult Health Longitudinal Study (Estudo Longitudinal de Saúde do Adulto - ELSA-Brasil), with more than 14,000 adults and elderly people (median age 51 years), identified higher multimorbidity prevalence among Brown (6.0%) and Black individuals (9.0%) when compared to White participants. When evaluating multimorbidity, considered as presence of six or more diseases, a study found that prevalence was 27.0% higher for Brown individuals and 47.0% higher for Black individuals compared to White individuals 12. However, in our study, lower multimorbidity prevalence was observed among Brown individuals compared to White individuals, and no statistically significant association was observed for Black individuals.
Race/skin color is a social category that highlights how non-dominant ethnic-racial groups face reduced access to resources and opportunities, accumulating disadvantages throughout life 25,26. Structural racism operates as a hierarchical system that privileges White people, generating intentional or unintentional inequalities 26. One theory about its effects on health suggests that socioeconomically disadvantaged individuals tend to adopt less healthy habits as a compensatory mechanism 7, such as smoking, and face barriers to incorporating healthy practices 6,7, exacerbated by the stress of discrimination. Although the findings of this study do not confirm this relationship, longitudinal data from the United States indicate that Black adults (Brown and Black) accumulate more chronic diseases at an earlier age than Whites, a result of early and continuous exposure to risk factors such as excess weight, stress and reduced access to healthcare 13.
In the intersectional analysis, multimorbidity prevalence was higher among White females, followed by Black and Brown females, than among White males, with no association between Brown and Black males. However, the ELSA-Brasil study results showed that prevalence was higher among Black females, followed by Brown and White females, when compared to White males, while Brown and Black males were not associated with higher occurrence of multimorbidity 12. These findings suggest a concurrent risk factor: mortality due to external causes is higher among Brown and Black people, especially men, at younger ages 2,27,28, which reduces the chance of multimorbidity occurring at older ages. In Brazil, premature mortality due to external causes and NCDs is higher among these groups 27. Furthermore, age adjustment led to the loss of statistical significance in the associations that included Black people, indicating a possible survival bias between females and White people 12. Thus, race/skin color and sex intersectionality interacts with other social determinants, impacting health conditions and favoring greater longevity among White people and females.
Considering that race/skin color and sex influence access to goods and services 15,18, education was used in this study as a proxy for socioeconomic status, which is strongly associated with employment and income 23. According to the 2022 IBGE census 28, education levels among White people are higher (29.2% in higher education vs. 15.3% among Black and Brown people), they have a lower unemployment rate and higher wages, which favors better living conditions and greater access to healthcare 23. When considering sex, although women have higher levels of education, they have lower incomes than men 23. The data from this study confirm these inequalities: Black and Brown people have less education than White people; and women of all races have a higher proportion of completed higher education than men. Even so, inequalities based on race/skin color and sex persist even after controlling for education, indicating that the social consequences of being dark-skinned or female have an independent and direct impact on poorer health outcomes.
The impact of sex and race/skin color on multimorbidity is clear 18. The theory of intersectionality broadens understanding of how individuals accumulate privileges or disadvantages throughout life depending on their social positions 15,18. Although sexism and racism act in isolation, their interaction intensifies inequalities and directly affects health 18. In Brazil, where NCDs are the leading cause of death 2, understanding these disparities is crucial to guiding effective strategies for vulnerable groups.
The National Policy on Comprehensive Health of the Black Population 27 aims to combat institutional racism in the Brazilian National Health System, while the National Policy on Comprehensive Health Care for Women 8 seeks to improve women’s living and health conditions, including specific actions for Black women. However, both policies lack concrete strategies to expand access to them, especially for Brown and Black women. In contrast, the National Policy on Comprehensive Health Care for Men 29 proposes actions in generally male-dominated spaces, such as soccer fields, bars and workshops, in addition to active outreach. The Strategic Action Plan for Addressing Chronic Diseases and Noncommunicable Diseases in Brazil 2021-2030 2 seeks to reduce premature mortality, but does not prioritize more vulnerable groups, such as Brown and Black men, who face greater barriers to access. Therefore, despite the relevance of these policies, an intersectoral dialogue is necessary to achieve concrete measures for promoting equity in access to health.
In this context, this study contributes by highlighting disparities according to sex and race/skin color in multimorbidity among Brazilian adults. A protective effect was identified among Brown males and higher prevalence among females. In the intersectional analysis, only White, Brown and Black females demonstrated higher prevalence than White males. These findings reinforce the need for a multidimensional approach to the social determinants of health, considering how race/skin color and sex structure health outcomes.
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17. Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Análise em Saúde e Vigilância de Doenças Não Transmissíveis. Vigitel Brasil 2018 População Negra: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas para a população negra nas capitais dos 26 estados brasileiros e no Distrito Federal em 2018 [Internet]. Brasília: Ministério da Saúde ; 2019 [cited 2025 Jul 20]. Available from: Available from: https://bvsms.saude.gov.br/bvs/publicacoes/vigitel_brasil_2018_populacao_negra.pdf
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19. Pesquisa Nacional de Saúde. Delineamento da PNS [Internet]. 2021 [cited 2025 Jul 23]. Available from: Available from: https://www.pns.icict.fiocruz.br/delineamento-da-pns/
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20. Stopa SR, Szwarcwald CL, Oliveira MM de, Gouvea ECDP, Vieira MLFP, Freitas MPS, et al. Pesquisa Nacional de Saúde 2019: histórico, métodos e perspectivas. Epidemiol Serv Saude. 2020;29(5)e2020315. https://doi.org/10.1590/S1679-49742020000500004
» https://doi.org/10.1590/S1679-49742020000500004 - 21. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa Nacional de Saúde 2019 [Internet] [cited 2025 Jul 22]. Available from:
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30. Tavares H, Coelho CG, Carioca AAF, Araújo LF. “Replication Data for: Disparidades Raciais e de Sexo Na Prevalência de Multimorbidade Por Doenças Crônicas No Brasil: Estudo Transversal.” Demo Dataverse. 2025. Available from: https://demo.dataverse.org/dataset.xhtml?persistentId=doi:10.70122/FK2/ZXZCGQ&faces-redirect=true.
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- Data availability
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Use of generative artificial intelligence
Not used.
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Funding
This study was funded in part by the Coordination for the Improvement of Higher Education Personnel - Brazil - Funding Code 001.
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Peer Review Administrator:
Izabela Fulone - https://orcid.org/0000-0002-3211-6951
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Peer Reviewers:
Fabiana Alves Soares - https://orcid.org/0000-0002-2940-8865
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Peer reviews:
https://doi.org/10.1590/S2237-96222026v35e20240697.a; https://doi.org/10.1590/S2237-96222026v35e20240697.b
Edited by
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Editor-in-Chief:
Jorge Otávio Maia Barreto - https://orcid.org/0000-0002-7648-0472
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Scientific Editor:
Wildo Navegantes de Araújo: - https://orcid.org/0000-0002-6856-4094
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Associate Editor:
Cristine Vieira do Bonfim - https://orcid.org/ 0000-0002-4495-9673
