The Paradox of Perceived Discrimination in Later Life: Evidence from a Nationally Representative Study in Brazil

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This study analyzed variations in perceived discrimination across age groups among Brazilian adults and older adults (≥50 years), using data from the Brazilian Longitudinal Study of Aging (ELSI-Brazil, 2019–2021). Methods: This is a cross-sectional, population-based study with probabilistic sampling and national representativeness. Perceived discrimination was measured using five items from the Everyday Discrimination Scale (EDS). Given the high proportion of zero counts (77.2%) and data overdispersion, a Zero-Inflated Negative Binomial (ZINB) model with robust variance was applied to estimate Incidence Rate Ratios (IRR), accounting for the complex survey design. Results:The Wald F test indicated significant differences across age groups (p=0.012), with the highest mean observed among participants aged 50–59 years (1.16; 95% CI: 0.87–1.45) and the lowest among those aged 80 years or older (0.69; 95% CI: 0.45–0.93). The ZINB model confirmed the adequacy of the approach (alpha: p<0.001; zero-inflation term: p<0.001). In the count process, age was a significant predictor: individuals aged 80 years or older had a 20% lower expected discrimination rate (IRR = 0.80; 95% CI: 0.67–0.97) compared to those aged 50–59 years. Conclusions: These findings support the "Paradox of Perceived Discrimination", indicating that reports of unfair treatment tend to decline with advancing age despite increased objective vulnerability. This trend potentially reflects psychosocial self-protection mechanisms and internalized age-related beliefs, suggesting the need for intersectional approaches in health policies. Ageism Social Discrimination Social Determinants of Health Background The guarantee of health as a fundamental human right underpins universal health systems worldwide. International organizations, such as the World Health Organization (WHO), advocate for the construction of systems that ensure comprehensive, universal, and equitable access to services, with Primary Health Care (PHC) as the central axis for equity 1 . In Brazil, this ideal is embodied in the Unified Health System (SUS), which, founded on the principles of universality, comprehensiveness, and equity 2 , reflects the commitment enshrined in the 1988 Federal Constitution 2 . However, the achievement of equity is constantly challenged by the structural inequities that permeate society 3 . Opportunities to live healthily are directly influenced by the Social Determinants of Health (SDH), according to the model proposed by the WHO, which considers the complex interaction between structural and intermediary factors 4 . SDH, such as income, schooling, gender, race, and territory, directly influence health outcomes, revealing persistent inequalities. For example, the chronic stress generated by adverse socioeconomic conditions and low control over life (psychosocial factors) are associated with negative health outcomes, such as increased prevalence of mental disorders and chronic diseases 5 . Understanding how these determinants operate is crucial, and in this scenario, perceived discrimination emerges as a fundamental SDH. Perceived discrimination is defined as subjective experiences of unfair treatment in everyday situations. This chronic exposure to psychosocial stress, often measured by general instruments such as the Everyday Discrimination Scale, can predict adverse health outcomes, regardless of access to services 6 . Chronic exposure to discriminatory events, even those of low intensity and frequency, has been associated with negative outcomes for physical and mental health, acting as a persistent psychosocial stressor 7 . Evidence indicates that experiences of prejudice related to age (ageism), gender, race/color, and socioeconomic status negatively affect health, reinforcing inequities in the social system 8 . It cannot be viewed merely as an individual event, but as a reflection of structural inequalities, where factors such as race/color, social class, and gender directly influence the experiences of unfair treatment 9 . Discrimination, in its various forms, constitutes a socially structured phenomenon that affects both individual and institutional experiences, and its analysis is fundamental for guiding actions aimed at equity 10 . The transition to old age intensifies exposure to the Social Determinants of Health (SDH), and in this context, perceived discrimination assumes an even more critical dimension. The older population is not homogenous: aging is a heterogeneous process, where chronological age intersects with factors such as race/color, socioeconomic status, gender, and culture, creating a complex web of intersectionality, adopted here as an interpretative lens, that shapes individual vulnerabilities 11 . Thus, the way discrimination is experienced and reported can vary significantly throughout later life, reflecting both social structures and cultural patterns that define the value of the individual 12 , 13 . Analyzing this variation is crucial, as it may reveal that the most acute inequities are not due to age alone, but rather to the intersection of multiple social stigmas. Therefore, the present study aims to analyze the variation in the perception of general discrimination by specific age groups in the adult and older Brazilian population, in order to contribute to the understanding of the inequities that affect this population. Methodology This is a cross-sectional, population-based study, developed from a secondary analysis of data from the Brazilian Longitudinal Study of Aging (ELSI-Brasil), conducted between 2019 and 2021 in 70 municipalities across all regions of the country 14 . Individuals aged 50 years or older were included, selected through a multi-stage probabilistic sampling process, ensuring national and regional representativeness 14 . For this study, only participants who responded to the module related to perceived discrimination were considered. The ELSI-Brasil sample was specifically designed to represent the non-institutionalized Brazilian population aged 50 years or older 14 . The complex sampling design was executed in multiple stages: in the first three strata of municipalities, selection occurred in three stages (municipalities, census tracts, and households), while in the fourth stratum (large municipalities), two stages were considered (census tracts and households). All residents aged 50 years or older found in the selected households were eligible, totaling approximately 10,000 participants. Calibrated sample weights were applied to ensure valid estimates for the Brazilian population 14 . Data were collected through in-person household interviews using standardized and structured questionnaires administered by trained interviewers 14 . Among the information collected were demographic and socioeconomic characteristics, health conditions, and the experiences of perceived discrimination in daily life 14 . The dependent variable was perceived discrimination, measured using five items from the Everyday Discrimination Scale (EDS), adapted in Brazil as the Escala de Discriminação no Dia a Dia (EDD) 6 . This instrument assesses the frequency of unfair treatment experiences in everyday situations. The score was calculated by summing the five questionnaire items that capture how often participants felt they had been treated unfairly or disrespectfully in social contexts. The items included in the scale were: Being treated with less kindness or courtesy than other people; Being treated with less respect or receiving poorer quality service; People acting as if they think you are not intelligent; People acting as if they are afraid of you; Being threatened or harassed. The original response options for each item ranged from 0 to 5. The lowest value, 0, corresponded to “never,” indicating the absence of perceived discrimination. The subsequent categories represented increasing frequencies of discriminatory experiences: 1 = less than once a year, 2 = a few times a year, 3 = a few times a month, 4 = once a week, and 5 = almost every day. The final score was obtained by summing these item values, generating a continuous discrete scale ranging from 0 to 25, in which higher scores indicate a greater perception of unfair treatment. The EDS has been validated and shown to be psychometrically robust in Brazil 15 , supporting its application in nationally representative samples such as ELSI-Brasil. Although the measurement invariance of the EDS has been confirmed for certain social groups, studies conducted in the United States highlight the need for caution when comparing mean scores across age groups 16 . The independent variable of interest was age group, originally recorded in the ELSI-Brasil dataset as the participant’s exact age in full years. This variable was categorized into four groups, following methodological and epidemiological practices commonly applied in ELSI-Brasil analyses: 50–59 years, 60–69 years, 70–79 years, and 80 years or older 14 . This categorization is essential for capturing specific health and functional transitions that occur across different stages of aging, from midlife to advanced old age. Covariates included in the adjustment set were race/color, sex, and household income, all treated as control variables in the subsequent regression models. Statistical analyses were conducted using Stata software, version 19.5, accounting for the complex survey design through the svy prefix. To obtain adjusted estimates of standard errors and confidence intervals, the Taylor linearization method (vce(linearized)) was applied, which is appropriate for survey data with complex sampling structures 17 . The analysis of mean discrimination scores across age groups was performed using Analysis of Variance (ANOVA) adjusted for complex survey data, implemented through the Wald F-test (Adjusted Wald Test), as it correctly accounts for the survey design features and provides robust variance estimates 18 . This decision was supported by the asymptotic robustness of the F-test, which given the large sample size is less sensitive to deviations from normality than to violations of homoscedasticity or independence of errors 19, 20 . The variance correction provided by the Taylor linearization method ensures statistical adjustment for the lack of independence among sample observations 17 and mitigates the effects of the complex sampling design 18 , thus maintaining the validity of the inferential results. When the global test indicated a statistically significant difference (p < 0.05), pairwise comparisons based on the Wald test were conducted to identify which age groups differed significantly from one another 18 . All analyses adopted a 5% significance level. The distribution of the discrimination score exhibited a high prevalence of zeros (77.2%) and significant overdispersion. Consequently, a Zero-Inflated Negative Binomial (ZINB) regression model with robust variance estimation was employed, given the non-random nature of the excess zeros and the complex sample design of the survey 21,22 . The ZINB model estimates two latent processes simultaneously: 1. a logistic component for the probability of structural zeros (never experiencing discrimination), and 2. a negative binomial count component for the intensity of discrimination among those susceptible. The ZINB approach was essential due to the high fraction of zero counts and the statistically significant overdispersion. The model was implemented using the full specification, incorporating all covariates in both the Count and the Zero-Inflated components, and all analyses accounted for the complex sample design through the svy prefix in Stata (StataCorp LLC, College Station, TX, USA). The primary hypothesis test focused on the main independent variable, age, using the 50–59 years group as the reference category. The primary outcome measure was the Incidence Rate Ratio (IRR) obtained from the Count Process. Model adequacy was assessed by examining the significance of the overdispersion parameter (alpha) and the intercept of the inflate block, confirming the statistical necessity of the ZINB approach over standard Poisson or Negative Binomial models. Furthermore, the Zero-Inflated component was specifically used to evaluate whether age and the control variables were significant predictors of structural zero status. For this process, the coefficients and respective 95% confidence intervals were exponentiated to report the appropriate Odds Ratios (OR). The ELSI-Brasil study was approved by the Ethics Committee of the Fundação Oswaldo Cruz - Minas Gerais. (CAAE: 34649814.3.0000.5091). Results The weighted analysis of the sample revealed that the final sample comprised 9,564 individuals aged 50 years or older. The descriptive distribution of the discrimination items showed that the most frequently reported situation was “being treated with less courtesy” , reported by 14.8% of the sample, followed by “people acting as if the respondent was not smart” (13.4%). Situations involving fear or threats were less common, reported by approximately 5.5% and 6.1% of the sample, respectively. The overall mean discrimination score in the studied population was 1.03 (SD = 2.52) (Table 1 ). Table 1 Prevalence of specific discrimination items and descriptive characteristics of the Everyday Discrimination Scale (EDS) among Brazilian adults and older adults (ELSI-Brazil, 2019–2021). Variable Prevalence of “Ever Experienced”a % (95% CI) Individual Items Treated with less courtesy 14.76 (13.2–16.5) People acted as if not smart 13.43 (12.0–15.0) Received poorer service 9.59 (8.4–10.9) Felt threatened or harassed 6.07 (5.1–7.2) People acted as if afraid 5.46 (4.6–6.5) Overall Discrimination Score 22.78 (20.9–24.8) Age Distribution – Proportion (%) 50–59 years 47.2 60–69 years 29.1 70–79 years 16.2 80 years or older 7.5 Notes: SD = Standard Deviation. Estimates calculated using complex survey weights. a Percentage of individuals who reported a frequency other than “Never” (Score ≥ 1). The analysis of mean discrimination scores across age groups indicated a statistically significant difference (p = 0.012). The mean discrimination scores ranged from 1.16 in the 50–59 years group to 0.69 among individuals aged 80 years or older. The post-hoc tests revealed that the oldest-old group presented the lowest mean score of perceived discrimination, which was significantly lower than that of the 50–59-year group (p = 0.002), as detailed in Table 2 . Table 2 Comparison of mean perceived discrimination scores stratified by age group (ELSI-Brazil, 2019–2021). Age GroupMean Score IC 95% p-value 50–59 years. 1.16 0.87–1.45 Ref. 60–69 years0,91 0.69–1.12 0.013 c 70–79 years1.03 0.70–1.37 0.398 c 80 years or older0.69 0.45–0.93 0.002 c Notes: Results based on linearized variance estimation ( svy: mean ). c P-value for the pairwise comparison against the reference group (50–59 years). The count outcome data, characterized by a substantial fraction of 77.2% zero observations, necessitated the implementation of the Zero-Inflated Negative Binomial (ZINB) model within a complex survey design framework. Statistical assessment of the ZINB model confirmed its necessity: the dispersion parameter (alpha) was significantly different from zero (F(1,269) = 20.59; p < 0.001), confirming overdispersion, and the intercept of the 'inflate' block was highly significant (F(1,269) = 63.03; p < 0.001), validating the presence of excess structural zeros and the superiority of the ZINB over simpler Negative Binomial or Poisson models. In the ZINB’s count process, which estimates the rate of events among the susceptible population, age emerged as a significant predictor only for the oldest age group. After adjustment for sociodemographic factors, individuals aged 80 years or older had a 20% lower expected rate of discrimination when compared to the reference group (Adjusted IRR = 0.80; 95% CI: 0.67–0.97; p = 0.021) (Table 3 ). No significant differences in intensity were observed for the 60–69 and 70–79-year groups in comparison to the 50–59-year group. Conversely, within the Zero-Inflated Process (‘inflate’ block), age, sex, and income were not statistically significant predictors, suggesting that the factors driving the distinction between structural zeros and sampling zeros remain unaccounted for by the included sociodemographic covariates. Table 3 Association between age and the intensity of perceived discrimination: Incidence Rate Ratios (IRR) estimated by the Zero-Inflated Negative Binomial (ZINB) model. Age Group Crude Analysis IRR (95% CI) Adjusted Analysis d IRR (95% CI) P-value (Adjusted) 50–59 years 1.00 (Reference) 1.00 (Reference) 60–69 years 0.88 (0.77–0.99) 0.89 (0.78–1.01) 0.060 70–79 years 1.02 (0.84–1.23) 1.01 (0.84–1.21) 0.951 80 years or older 0.79 (0.65–0.96) 0.80 (0.67–0.97) 0.021 Notes: IRR = Incidence Rate Ratio; 95% CI = 95% Confidence Interval. d Adjusted for sex, self-reported race/colour, and household income. Model Diagnostics: • Dispersion test (alpha): p < 0.001 • Zero-inflation test: p < 0.001 Discussion This population-based study found a significant prevalence of perceived discrimination in daily life, with variation across age groups: younger individuals (50–59 years) reported higher levels of discrimination, whereas those aged 80 years and older presented the lowest mean scores. The use of the Everyday Discrimination Scale (EDS) reinforces the need to interpret these findings through the lens of intersectionality, which recognizes how different axes of marginalization (age, race, and social class) intersect to shape inequitable experiences 23 . The higher report of perceived discrimination among the 50–59 age group suggests that the main source of inequity for this cohort stems from the intersection of social vulnerabilities, rather than chronological age alone. At this life stage, individuals are often more socially active but also more exposed to stigmas related to socioeconomic and class conditions. The chronic stress captured by the EDS (e.g., “receiving poorer quality service” or “being treated with less respect”) may reflect financial insecurity and dependency on public services. This group, in transition between adulthood and old age, tends to be more vigilant and sensitive to signs of stigma, which amplifies the perception of unfair treatment 24 . Thus, the greater perception of inequity in this cohort is linked to the intersection of class markers and early aging 23 . In contrast, the finding that the group aged 80 years or older showed the lowest mean of perceived discrimination reflects the Paradox of Perceived Discrimination in Older Adults 25 . This paradox suggests that the perception of discrimination decreases with advancing age, despite increased vulnerability and exposure to ageism. Although not directly assessed in our data, the literature suggests that the central mechanism behind this discrepancy lies in cognitive self-protective strategies that preserve self-esteem 26 . Self-directed ageism was primarily determined by the mental and physical health status of older adults. This finding suggests the bidirectional nature of the association between self-directed ageism and health. Little is known about the processes that connect age stereotypes to self-stereotypes, especially regarding how negative stereotypes widely present in society become self-directed ageism through their self-relevance. This internalization of negative stereotypes may influence not only self-image but also how older individuals perceive, or fail to perceive situations of discrimination, shaping their interpretation of events and their positioning in relation to age-related inequalities 27 . For the oldest adults, avoiding attributing negative treatment to ageism may function as a defense mechanism that helps maintain a positive self-image 28 . Older individuals may dissociate themselves from the stereotype of frailty 14 , which prevents them from identifying as legitimate targets of discrimination. Alternatively, they may readjust their standards of evaluation through downward social comparison (with peers in worse health conditions), leading to lower feelings of injustice and the maintenance of subjective well-being 29 . Therefore, the lower discrimination score observed among the 80 + group does not indicate the absence of inequity, but rather the effectiveness of psychosocial mechanisms in mitigating the perception of injustice. The main strength of this study lies in the use of data from ELSI-Brazil, a nationally representative, population-based longitudinal study. The core finding of the study reveals that perceived discrimination functions as a complex social stressor whose intensity and nature vary according to the stage of aging. The primary methodological contribution of this study is the application of the Zero-Inflated Negative Binomial (ZINB) model within a complex survey design framework. The substantial proportion of 75% zero counts and the observed overdispersion rendered simpler models, such as Poisson or standard Negative Binomial regression, inappropriate, as they would yield underestimated standard errors and inflated conclusions. Second, the use of the Wald F Test (adjusted ANOVA) and Taylor Linearization ensures robust estimates when handling the complex sampling design 17 , 18 . However, the cross-sectional nature of this analysis prevents causal inference. Additionally, because the discrimination variable is subjective, it may be affected by recall bias, and as psychometric research suggests 16 , the potential lack of full scalar invariance of the EDS across age groups requires caution when interpreting mean differences although robust statistical procedures were applied. Finally, despite age being a significant predictor in the Count Process, its lack of significance in the Zero-Inflated block (non-significant ORs) indicates that age and the adjusting covariates (race, sex, and income) do not effectively differentiate between structural zeros and sampling zeros. In other words, these factors are poor predictors of the outcome status. This limitation suggests that membership in the 'structural zero' group may be driven by unmeasured confounders or latent factors, which operate independently of sociodemographic variables. Conclusion This study demonstrated that perceived discrimination is a significant phenomenon among Brazilian adults and older adults, showing distinct variations across age groups. The central finding the Paradox of Perceived Discrimination indicates that younger individuals (50–59 years) reported the highest levels of discrimination, while the oldest group (80 + years) presented the lowest means. Thus, perceived discrimination operates as a social determinant of health in a complex and nonlinear way throughout the aging process. It is essential that public policies, healthcare systems and researchers adopt an intersectional approach, to investigate and address health and social inequities, acknowledging that experiences of unfair treatment are not solely age-related. This analytical lens is crucial for informing more effective and targeted social and health policies and combating multiple forms of stigma. Declarations Ethics approval and consent to participate: The ELSI-Brazil study was approved by the Research Ethics Committee of the Fundação Oswaldo Cruz (Fiocruz-Minas) (CAAE: 34649814.3.0000.5091). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments. Written informed consent was obtained from all individual participants included in the study. Consent for publication: Not applicable. Availability of data and materials The datasets generated and/or analysed during the current study are available in the ELSI-Brazil repository, https://elsi.cpqrr.fiocruz.br/en/access-to-data/. Competing interests: The authors declare that they have no competing interests. Funding: The ELSI-Brazil baseline study was funded by the Brazilian Ministry of Health (DECIT/SCTIE) (processes: 404965/2012-1 and TED 28/2017). The funding body had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Authors' contributions: JMAG conceived and designed the study and provided methodological guidance. ASR was responsible for data analysis, interpretation of results, and drafting the initial version of the manuscript. OLAJ, MBG, and MLBF contributed to the interpretation of the data and critically reviewed the manuscript for important intellectual content. JMAG supervised the study and critically reviewed the final manuscript. All authors read and approved the final manuscript. Acknowledgements: We would like to thank the participants and research staff of the Brazilian Longitudinal Study of Aging (ELSI-Brazil) for their cooperation and contribution to this study. Declaration of Generative AI and AI-assisted technologies in the writing process: During the preparation of this work the authors used large language models (LLMs) to improve the clarity and flow of the English language throughout the manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. References World Health Organization. Primary health care: a guide for countries on taking action. Geneva: WHO; 2021. Brasil. Constituição da República Federativa do Brasil. Brasília: Senado Federal; 1988. Carrapato P, Correia P, Garcia B. Determinante da saúde no Brasil: a procura da equidade na saúde. Saúde Soc. 2017;26(3):676–89. Solar O, Irwin A. A conceptual framework for action on the social determinants of health. Geneva: World Health Organization; 2010. (Social Determinants of Health Discussion Paper 2 – Policy and Practice). Marmot M, et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8866149","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596486560,"identity":"a1ed3e37-bc1c-42ca-99ad-ea8c7d0be795","order_by":0,"name":"Ariele Saldanha Rodrigues","email":"","orcid":"","institution":"Universidade Federal de Santa Maria (UFSM)","correspondingAuthor":false,"prefix":"","firstName":"Ariele","middleName":"Saldanha","lastName":"Rodrigues","suffix":""},{"id":596486561,"identity":"499edf96-c60d-413f-8228-ab49163aba1f","order_by":1,"name":"Magáli Beck Guimarães","email":"","orcid":"","institution":"Universidade Federal de Santa Maria (UFSM)","correspondingAuthor":false,"prefix":"","firstName":"Magáli","middleName":"Beck","lastName":"Guimarães","suffix":""},{"id":596486563,"identity":"cc24d226-c735-4560-ab60-4a59459732bf","order_by":2,"name":"Orlando Luiz do Amaral Júnior","email":"","orcid":"","institution":"Universidade Federal de Santa Maria (UFSM)","correspondingAuthor":false,"prefix":"","firstName":"Orlando","middleName":"Luiz do Amaral","lastName":"Júnior","suffix":""},{"id":596486564,"identity":"26b35668-f22c-4a38-a8a6-0e3ca9d7624f","order_by":3,"name":"Maria Laura Braccini Fagundes","email":"","orcid":"","institution":"Universidade Federal de Santa Maria (UFSM)","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Laura Braccini","lastName":"Fagundes","suffix":""},{"id":596486568,"identity":"8dabb831-5a12-4291-8d3d-3d96a6bf812e","order_by":4,"name":"Jessye Melgarejo do Amaral Giordani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBACxgYogw2IJYBIjoH5AJIwMVqMGdgS8GtBAUAtDIkNhLQwt7c//sxTwWDXx9778MbPHRbpG46xP93AuOMebof1nDGT5jnDkNzGc9zYsveMRO6GYzxmNxjPFOPWMiOHjZm3jSGZTSKNTYK3Dajlfg/bDca2BNxa5j9//Jn3H0SL5N82iXSDY+zP8GuZwWAgzdvAYAfSIg20JcHgGIMZfi09OWaSc45JJLDxHGO2lm2TMJwJ8kviGdxaDNuPP/7wpsbGXr69jfHm27Y6eT6Qwz7uwKOlgYGBiYdBIrEBRRi3BgYGeZDjfjAw2ONRMwpGwSgYBSMdAACMyVEgE1/b/QAAAABJRU5ErkJggg==","orcid":"","institution":"Universidade Federal de Santa Maria (UFSM)","correspondingAuthor":true,"prefix":"","firstName":"Jessye","middleName":"Melgarejo do Amaral","lastName":"Giordani","suffix":""}],"badges":[],"createdAt":"2026-02-13 00:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8866149/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8866149/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104399137,"identity":"7db36628-595e-4abb-9050-328a6a20115a","added_by":"auto","created_at":"2026-03-11 12:04:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":461404,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8866149/v1/5a47f475-0a0d-4b5c-8bdd-155b98060fad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Paradox of Perceived Discrimination in Later Life: Evidence from a Nationally Representative Study in Brazil","fulltext":[{"header":"Background","content":"\u003cp\u003eThe guarantee of health as a fundamental human right underpins universal health systems worldwide. International organizations, such as the World Health Organization (WHO), advocate for the construction of systems that ensure comprehensive, universal, and equitable access to services, with Primary Health Care (PHC) as the central axis for equity \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In Brazil, this ideal is embodied in the Unified Health System (SUS), which, founded on the principles of universality, comprehensiveness, and equity \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, reflects the commitment enshrined in the 1988 Federal Constitution \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the achievement of equity is constantly challenged by the structural inequities that permeate society \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Opportunities to live healthily are directly influenced by the Social Determinants of Health (SDH), according to the model proposed by the WHO, which considers the complex interaction between structural and intermediary factors \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. SDH, such as income, schooling, gender, race, and territory, directly influence health outcomes, revealing persistent inequalities. For example, the chronic stress generated by adverse socioeconomic conditions and low control over life (psychosocial factors) are associated with negative health outcomes, such as increased prevalence of mental disorders and chronic diseases \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Understanding how these determinants operate is crucial, and in this scenario, perceived discrimination emerges as a fundamental SDH.\u003c/p\u003e \u003cp\u003ePerceived discrimination is defined as subjective experiences of unfair treatment in everyday situations. This chronic exposure to psychosocial stress, often measured by general instruments such as the Everyday Discrimination Scale, can predict adverse health outcomes, regardless of access to services \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Chronic exposure to discriminatory events, even those of low intensity and frequency, has been associated with negative outcomes for physical and mental health, acting as a persistent psychosocial stressor \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Evidence indicates that experiences of prejudice related to age (ageism), gender, race/color, and socioeconomic status negatively affect health, reinforcing inequities in the social system \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. It cannot be viewed merely as an individual event, but as a reflection of structural inequalities, where factors such as race/color, social class, and gender directly influence the experiences of unfair treatment \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Discrimination, in its various forms, constitutes a socially structured phenomenon that affects both individual and institutional experiences, and its analysis is fundamental for guiding actions aimed at equity \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe transition to old age intensifies exposure to the Social Determinants of Health (SDH), and in this context, perceived discrimination assumes an even more critical dimension. The older population is not homogenous: aging is a heterogeneous process, where chronological age intersects with factors such as race/color, socioeconomic status, gender, and culture, creating a complex web of intersectionality, adopted here as an interpretative lens, that shapes individual vulnerabilities \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Thus, the way discrimination is experienced and reported can vary significantly throughout later life, reflecting both social structures and cultural patterns that define the value of the individual \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, 13\u003c/sup\u003e. Analyzing this variation is crucial, as it may reveal that the most acute inequities are not due to age alone, but rather to the intersection of multiple social stigmas.\u003c/p\u003e \u003cp\u003eTherefore, the present study aims to analyze the variation in the perception of general discrimination by specific age groups in the adult and older Brazilian population, in order to contribute to the understanding of the inequities that affect this population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e "},{"header":"Methodology","content":"\u003cp\u003eThis is a cross-sectional, population-based study, developed from a secondary analysis of data from the Brazilian Longitudinal Study of Aging (ELSI-Brasil), conducted between 2019 and 2021 in 70 municipalities across all regions of the country \u003csup\u003e14\u003c/sup\u003e. Individuals aged 50 years or older were included, selected through a multi-stage probabilistic sampling process, ensuring national and regional representativeness \u003csup\u003e14\u003c/sup\u003e. For this study, only participants who responded to the module related to perceived discrimination were considered.\u003c/p\u003e\n\u003cp\u003eThe ELSI-Brasil sample was specifically designed to represent the non-institutionalized Brazilian population aged 50 years or older \u003csup\u003e14\u003c/sup\u003e. The complex sampling design was executed in multiple stages: in the first three strata of municipalities, selection occurred in three stages (municipalities, census tracts, and households), while in the fourth stratum (large municipalities), two stages were considered (census tracts and households). All residents aged 50 years or older found in the selected households were eligible, totaling approximately 10,000 participants. Calibrated sample weights were applied to ensure valid estimates for the Brazilian population \u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eData were collected through in-person household interviews using standardized and structured questionnaires administered by trained interviewers \u003csup\u003e14\u003c/sup\u003e. Among the information collected were demographic and socioeconomic characteristics, health conditions, and the experiences of perceived discrimination in daily life \u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe dependent variable was perceived discrimination, measured using five items from the Everyday Discrimination Scale (EDS), adapted in Brazil as the Escala de Discrimina\u0026ccedil;\u0026atilde;o no Dia a Dia (EDD) \u003csup\u003e6\u003c/sup\u003e. This instrument assesses the frequency of unfair treatment experiences in everyday situations.\u003c/p\u003e\n\u003cp\u003eThe score was calculated by summing the five questionnaire items that capture how often participants felt they had been treated unfairly or disrespectfully in social contexts. The items included in the scale were:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003e\n \u003cp\u003eBeing treated with less kindness or courtesy than other people;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eBeing treated with less respect or receiving poorer quality service;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePeople acting as if they think you are not intelligent;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePeople acting as if they are afraid of you;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eBeing threatened or harassed.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe original response options for each item ranged from 0 to 5. The lowest value, 0, corresponded to \u0026ldquo;never,\u0026rdquo; indicating the absence of perceived discrimination. The subsequent categories represented increasing frequencies of discriminatory experiences: 1\u0026thinsp;=\u0026thinsp;less than once a year, 2\u0026thinsp;=\u0026thinsp;a few times a year, 3\u0026thinsp;=\u0026thinsp;a few times a month, 4\u0026thinsp;=\u0026thinsp;once a week, and 5\u0026thinsp;=\u0026thinsp;almost every day.\u003c/p\u003e\n\u003cp\u003eThe final score was obtained by summing these item values, generating a continuous discrete scale ranging from 0 to 25, in which higher scores indicate a greater perception of unfair treatment. The EDS has been validated and shown to be psychometrically robust in Brazil \u003csup\u003e15\u003c/sup\u003e, supporting its application in nationally representative samples such as ELSI-Brasil. Although the measurement invariance of the EDS has been confirmed for certain social groups, studies conducted in the United States highlight the need for caution when comparing mean scores across age groups \u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe independent variable of interest was age group, originally recorded in the ELSI-Brasil dataset as the participant\u0026rsquo;s exact age in full years. This variable was categorized into four groups, following methodological and epidemiological practices commonly applied in ELSI-Brasil analyses: 50\u0026ndash;59 years, 60\u0026ndash;69 years, 70\u0026ndash;79 years, and 80 years or older \u003csup\u003e14\u003c/sup\u003e. This categorization is essential for capturing specific health and functional transitions that occur across different stages of aging, from midlife to advanced old age. Covariates included in the adjustment set were race/color, sex, and household income, all treated as control variables in the subsequent regression models.\u003c/p\u003e\n\u003cp\u003eStatistical analyses were conducted using Stata software, version 19.5, accounting for the complex survey design through the svy prefix. To obtain adjusted estimates of standard errors and confidence intervals, the Taylor linearization method (vce(linearized)) was applied, which is appropriate for survey data with complex sampling structures \u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe analysis of mean discrimination scores across age groups was performed using Analysis of Variance (ANOVA) adjusted for complex survey data, implemented through the Wald F-test (Adjusted Wald Test), as it correctly accounts for the survey design features and provides robust variance estimates \u003csup\u003e18\u003c/sup\u003e. This decision was supported by the asymptotic robustness of the F-test, which given the large sample size is less sensitive to deviations from normality than to violations of homoscedasticity or independence of errors \u003csup\u003e19, 20\u003c/sup\u003e. The variance correction provided by the Taylor linearization method ensures statistical adjustment for the lack of independence among sample observations \u003csup\u003e17\u003c/sup\u003e and mitigates the effects of the complex sampling design \u003csup\u003e18\u003c/sup\u003e, thus maintaining the validity of the inferential results. When the global test indicated a statistically significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), pairwise comparisons based on the Wald test were conducted to identify which age groups differed significantly from one another \u003csup\u003e18\u003c/sup\u003e. All analyses adopted a 5% significance level.\u003c/p\u003e\n\u003cp\u003eThe distribution of the discrimination score exhibited a high prevalence of zeros (77.2%) and significant overdispersion. Consequently, a Zero-Inflated Negative Binomial (ZINB) regression model with robust variance estimation was employed, given the non-random nature of the excess zeros and the complex sample design of the survey \u003csup\u003e21,22\u003c/sup\u003e. The ZINB model estimates two latent processes simultaneously: 1. a logistic component for the probability of \u003cem\u003estructural zeros\u003c/em\u003e (never experiencing discrimination), and 2. a negative binomial count component for the intensity of discrimination among those susceptible. The ZINB approach was essential due to the high fraction of zero counts and the statistically significant overdispersion. The model was implemented using the full specification, incorporating all covariates in both the Count and the Zero-Inflated components, and all analyses accounted for the complex sample design through the \u003cem\u003esvy\u003c/em\u003e prefix in Stata (StataCorp LLC, College Station, TX, USA). The primary hypothesis test focused on the main independent variable, age, using the 50\u0026ndash;59 years group as the reference category. The primary outcome measure was the Incidence Rate Ratio (IRR) obtained from the Count Process.\u003c/p\u003e\n\u003cp\u003eModel adequacy was assessed by examining the significance of the overdispersion parameter (alpha) and the intercept of the inflate block, confirming the statistical necessity of the ZINB approach over standard Poisson or Negative Binomial models. Furthermore, the Zero-Inflated component was specifically used to evaluate whether age and the control variables were significant predictors of structural zero status. For this process, the coefficients and respective 95% confidence intervals were exponentiated to report the appropriate Odds Ratios (OR).\u003c/p\u003e\n\u003cp\u003eThe ELSI-Brasil study was approved by the Ethics Committee of the Funda\u0026ccedil;\u0026atilde;o Oswaldo Cruz - Minas Gerais. (CAAE: 34649814.3.0000.5091).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe weighted analysis of the sample revealed that the final sample comprised 9,564 individuals aged 50 years or older. The descriptive distribution of the discrimination items showed that the most frequently reported situation was \u003cem\u003e\u0026ldquo;being treated with less courtesy\u0026rdquo;\u003c/em\u003e, reported by 14.8% of the sample, followed by \u003cem\u003e\u0026ldquo;people acting as if the respondent was not smart\u0026rdquo;\u003c/em\u003e (13.4%). Situations involving fear or threats were less common, reported by approximately 5.5% and 6.1% of the sample, respectively. The overall mean discrimination score in the studied population was 1.03 (SD\u0026thinsp;=\u0026thinsp;2.52) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of specific discrimination items and descriptive characteristics of the Everyday Discrimination Scale (EDS) among Brazilian adults and older adults (ELSI-Brazil, 2019\u0026ndash;2021).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrevalence of \u0026ldquo;Ever Experienced\u0026rdquo;a % (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual Items\u003c/p\u003e \u003cp\u003eTreated with less courtesy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.76 (13.2\u0026ndash;16.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeople acted as if not smart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.43 (12.0\u0026ndash;15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived poorer service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.59 (8.4\u0026ndash;10.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFelt threatened or harassed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.07 (5.1\u0026ndash;7.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeople acted as if afraid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.46 (4.6\u0026ndash;6.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Discrimination Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.78 (20.9\u0026ndash;24.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Distribution \u0026ndash; Proportion (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80 years or older\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eNotes: SD\u0026thinsp;=\u0026thinsp;Standard Deviation. Estimates calculated using complex survey weights. a Percentage of individuals who reported a frequency other than \u0026ldquo;Never\u0026rdquo; (Score\u0026thinsp;\u0026ge;\u0026thinsp;1).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis of mean discrimination scores across age groups indicated a statistically significant difference (p\u0026thinsp;=\u0026thinsp;0.012). The mean discrimination scores ranged from 1.16 in the 50\u0026ndash;59 years group to 0.69 among individuals aged 80 years or older. The post-hoc tests revealed that the oldest-old group presented the lowest mean score of perceived discrimination, which was significantly lower than that of the 50\u0026ndash;59-year group (p\u0026thinsp;=\u0026thinsp;0.002), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of mean perceived discrimination scores stratified by age group (ELSI-Brazil, 2019\u0026ndash;2021).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge GroupMean Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59 years. 1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87\u0026ndash;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69 years0,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69\u0026ndash;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79 years1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70\u0026ndash;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.398 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80 years or older0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45\u0026ndash;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: Results based on linearized variance estimation (\u003cem\u003esvy: mean\u003c/em\u003e).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003ec P-value for the pairwise comparison against the reference group (50\u0026ndash;59 years).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe count outcome data, characterized by a substantial fraction of 77.2% zero observations, necessitated the implementation of the Zero-Inflated Negative Binomial (ZINB) model within a complex survey design framework. Statistical assessment of the ZINB model confirmed its necessity: the dispersion parameter (alpha) was significantly different from zero (F(1,269)\u0026thinsp;=\u0026thinsp;20.59; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming overdispersion, and the intercept of the 'inflate' block was highly significant (F(1,269)\u0026thinsp;=\u0026thinsp;63.03; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), validating the presence of excess structural zeros and the superiority of the ZINB over simpler Negative Binomial or Poisson models.\u003c/p\u003e \u003cp\u003eIn the ZINB\u0026rsquo;s count process, which estimates the rate of events among the susceptible population, age emerged as a significant predictor only for the oldest age group. After adjustment for sociodemographic factors, individuals aged 80 years or older had a 20% lower expected rate of discrimination when compared to the reference group (Adjusted IRR\u0026thinsp;=\u0026thinsp;0.80; 95% CI: 0.67\u0026ndash;0.97; p\u0026thinsp;=\u0026thinsp;0.021) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). No significant differences in intensity were observed for the 60\u0026ndash;69 and 70\u0026ndash;79-year groups in comparison to the 50\u0026ndash;59-year group. Conversely, within the Zero-Inflated Process (\u0026lsquo;inflate\u0026rsquo; block), age, sex, and income were not statistically significant predictors, suggesting that the factors driving the distinction between structural zeros and sampling zeros remain unaccounted for by the included sociodemographic covariates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between age and the intensity of perceived discrimination: Incidence Rate Ratios (IRR) estimated by the Zero-Inflated Negative Binomial (ZINB) model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude Analysis\u003c/p\u003e \u003cp\u003eIRR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted Analysis d\u003c/p\u003e \u003cp\u003eIRR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003cp\u003e(Adjusted)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88 (0.77\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89 (0.78\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.84\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (0.84\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80 years or older\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.65\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80 (0.67\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: IRR\u0026thinsp;=\u0026thinsp;Incidence Rate Ratio; 95% CI\u0026thinsp;=\u0026thinsp;95% Confidence Interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ed Adjusted for sex, self-reported race/colour, and household income.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel Diagnostics:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u0026bull; Dispersion test (alpha): p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u0026bull; Zero-inflation test: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis population-based study found a significant prevalence of perceived discrimination in daily life, with variation across age groups: younger individuals (50\u0026ndash;59 years) reported higher levels of discrimination, whereas those aged 80 years and older presented the lowest mean scores. The use of the \u003cem\u003eEveryday Discrimination Scale\u003c/em\u003e (EDS) reinforces the need to interpret these findings through the lens of intersectionality, which recognizes how different axes of marginalization (age, race, and social class) intersect to shape inequitable experiences \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe higher report of perceived discrimination among the 50\u0026ndash;59 age group suggests that the main source of inequity for this cohort stems from the intersection of social vulnerabilities, rather than chronological age alone. At this life stage, individuals are often more socially active but also more exposed to stigmas related to socioeconomic and class conditions. The chronic stress captured by the EDS (e.g., \u0026ldquo;receiving poorer quality service\u0026rdquo; or \u0026ldquo;being treated with less respect\u0026rdquo;) may reflect financial insecurity and dependency on public services. This group, in transition between adulthood and old age, tends to be more vigilant and sensitive to signs of stigma, which amplifies the perception of unfair treatment\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Thus, the greater perception of inequity in this cohort is linked to the intersection of class markers and early aging \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn contrast, the finding that the group aged 80 years or older showed the lowest mean of perceived discrimination reflects the \u003cem\u003eParadox of Perceived Discrimination in Older Adults\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. This paradox suggests that the perception of discrimination decreases with advancing age, despite increased vulnerability and exposure to ageism. Although not directly assessed in our data, the literature suggests that the central mechanism behind this discrepancy lies in cognitive self-protective strategies that preserve self-esteem \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSelf-directed ageism was primarily determined by the mental and physical health status of older adults. This finding suggests the bidirectional nature of the association between self-directed ageism and health. Little is known about the processes that connect age stereotypes to self-stereotypes, especially regarding how negative stereotypes widely present in society become self-directed ageism through their self-relevance.\u003c/p\u003e \u003cp\u003eThis internalization of negative stereotypes may influence not only self-image but also how older individuals perceive, or fail to perceive situations of discrimination, shaping their interpretation of events and their positioning in relation to age-related inequalities\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor the oldest adults, avoiding attributing negative treatment to ageism may function as a defense mechanism that helps maintain a positive self-image \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Older individuals may dissociate themselves from the stereotype of frailty \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, which prevents them from identifying as legitimate targets of discrimination. Alternatively, they may readjust their standards of evaluation through downward social comparison (with peers in worse health conditions), leading to lower feelings of injustice and the maintenance of subjective well-being \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Therefore, the lower discrimination score observed among the 80\u0026thinsp;+\u0026thinsp;group does not indicate the absence of inequity, but rather the effectiveness of psychosocial mechanisms in mitigating the perception of injustice.\u003c/p\u003e \u003cp\u003eThe main strength of this study lies in the use of data from ELSI-Brazil, a nationally representative, population-based longitudinal study. The core finding of the study reveals that perceived discrimination functions as a complex social stressor whose intensity and nature vary according to the stage of aging. The primary methodological contribution of this study is the application of the Zero-Inflated Negative Binomial (ZINB) model within a complex survey design framework. The substantial proportion of 75% zero counts and the observed overdispersion rendered simpler models, such as Poisson or standard Negative Binomial regression, inappropriate, as they would yield underestimated standard errors and inflated conclusions. Second, the use of the Wald F Test (adjusted ANOVA) and Taylor Linearization ensures robust estimates when handling the complex sampling design \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the cross-sectional nature of this analysis prevents causal inference. Additionally, because the discrimination variable is subjective, it may be affected by recall bias, and as psychometric research suggests \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, the potential lack of full scalar invariance of the EDS across age groups requires caution when interpreting mean differences although robust statistical procedures were applied. Finally, despite age being a significant predictor in the Count Process, its lack of significance in the Zero-Inflated block (non-significant ORs) indicates that age and the adjusting covariates (race, sex, and income) do not effectively differentiate between structural zeros and sampling zeros. In other words, these factors are poor predictors of the outcome status. This limitation suggests that membership in the 'structural zero' group may be driven by unmeasured confounders or latent factors, which operate independently of sociodemographic variables.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that perceived discrimination is a significant phenomenon among Brazilian adults and older adults, showing distinct variations across age groups. The central finding the \u003cem\u003eParadox of Perceived Discrimination\u003c/em\u003e indicates that younger individuals (50\u0026ndash;59 years) reported the highest levels of discrimination, while the oldest group (80\u0026thinsp;+\u0026thinsp;years) presented the lowest means. Thus, perceived discrimination operates as a social determinant of health in a complex and nonlinear way throughout the aging process.\u003c/p\u003e \u003cp\u003eIt is essential that public policies, healthcare systems and researchers adopt an intersectional approach, to investigate and address health and social inequities, acknowledging that experiences of unfair treatment are not solely age-related. This analytical lens is crucial for informing more effective and targeted social and health policies and combating multiple forms of stigma.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: The ELSI-Brazil study was approved by the Research Ethics Committee of the Fundação Oswaldo Cruz (Fiocruz-Minas) (CAAE: 34649814.3.0000.5091). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments. Written informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials The datasets generated and/or analysed during the current study are available in the ELSI-Brazil repository, https://elsi.cpqrr.fiocruz.br/en/access-to-data/.\u003c/p\u003e\n\u003cp\u003eCompeting interests: The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding: The ELSI-Brazil baseline study was funded by the Brazilian Ministry of Health (DECIT/SCTIE) (processes: 404965/2012-1 and TED 28/2017). The funding body had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.\u003c/p\u003e\n\u003cp\u003eAuthors' contributions: JMAG conceived and designed the study and provided methodological guidance. ASR was responsible for data analysis, interpretation of results, and drafting the initial version of the manuscript. OLAJ, MBG, and MLBF contributed to the interpretation of the data and critically reviewed the manuscript for important intellectual content. JMAG supervised the study and critically reviewed the final manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements: We would like to thank the participants and research staff of the Brazilian Longitudinal Study of Aging (ELSI-Brazil) for their cooperation and contribution to this study.\u003c/p\u003e\n\u003cp\u003eDeclaration of Generative AI and AI-assisted technologies in the writing process: During the preparation of this work the authors used large language models (LLMs) to improve the clarity and flow of the English language throughout the manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Primary health care: a guide for countries on taking action. Geneva: WHO; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrasil. Constitui\u0026ccedil;\u0026atilde;o da Rep\u0026uacute;blica Federativa do Brasil. Bras\u0026iacute;lia: Senado Federal; 1988.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarrapato P, Correia P, Garcia B. Determinante da sa\u0026uacute;de no Brasil: a procura da equidade na sa\u0026uacute;de. 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Mapping the margins: intersecting identities, violence, and law. Stanf Law Rev. 1991;43(6):1241\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeri AL. O desenvolvimento humano e a psicologia do envelhecimento. In: Neri AL, editor. Psicologia do envelhecimento: temas de gerontologia. Campinas: Papirus; 2001. pp. 13\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDebert GG. A reinven\u0026ccedil;\u0026atilde;o da velhice: socializa\u0026ccedil;\u0026atilde;o e processos de reprivatiza\u0026ccedil;\u0026atilde;o do envelhecimento. Pro-Posi\u0026ccedil;\u0026otilde;es. 2004;15(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLima-Costa MF, et al. The Brazilian Longitudinal Study of Aging (ELSI-Brazil): objectives and design. Am J Epidemiol. 2018;187(7):1345\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBastos JL, Harnois CE. 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Biometrika. 1953;40(3/4):318\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBox GEP, Andersen SL. Permutation theory in the derivation of robust criteria and the study of departures from assumption. J R Stat Soc Ser B (Methodological). 1955;17(1):1\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLambert D. Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics. 1992;34(1):1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Lynch SM, Sarkar S. Comparison of zero-inflated models for count data: case study in health services research. J Data Sci. 2015;13(4):555\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrueger H, Williams SK. The perception of age discrimination in the workplace by older workers: an international analysis. J Bus Ethics. 2012;111(1):1\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarnes LL, et al. Perceived discrimination and incident functional disability in older adults: a 4-year prospective study. J Gerontol B Psychol Sci Soc Sci. 2008;63B(1):S10\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFattore F, et al. Ageism and discrimination in old age: a narrative review of the literature. Gerontologist. 2012;52(4):488\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrocker J, Major B. Social stigma and self-esteem: the self-protective properties of stigma. Psychol Rev. 1989;96(4):608\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshikawa M. Internalization of negative societal views on old age into self-perceptions of aging: exploring factors associated with self-directed ageism. Front Sociol. 2023;8:1291325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fsoc.2023.1291325\u003c/span\u003e\u003cspan address=\"10.3389/fsoc.2023.1291325\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeckhausen J, Schulz R. A life-span theory of control. Psychol Rev. 1995;102(2):284\u0026ndash;304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaltes PB, Baltes MM. Psychological perspectives on successful aging: The model of selective optimization with compensation. In: Baltes PB, Baltes MM, editors. Successful Aging: Perspectives from the Behavioral Sciences. Cambridge: Cambridge University Press; 1990. pp. 1\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ageism, Social Discrimination, Social Determinants of Health","lastPublishedDoi":"10.21203/rs.3.rs-8866149/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8866149/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Perceived discrimination is a social determinant of health associated with poorer physical and mental outcomes, reflecting structural inequalities that manifest intersectionally through age, gender, race, and social class. This study analyzed variations in perceived discrimination across age groups among Brazilian adults and older adults (≥50 years), using data from the Brazilian Longitudinal Study of Aging (ELSI-Brazil, 2019–2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: This is a cross-sectional, population-based study with probabilistic sampling and national representativeness. Perceived discrimination was measured using five items from the Everyday Discrimination Scale (EDS). Given the high proportion of zero counts (77.2%) and data overdispersion, a Zero-Inflated Negative Binomial (ZINB) model with robust variance was applied to estimate Incidence Rate Ratios (IRR), accounting for the complex survey design.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults:The Wald F test indicated significant differences across age groups (p=0.012), with the highest mean observed among participants aged 50–59 years (1.16; 95% CI: 0.87–1.45) and the lowest among those aged 80 years or older (0.69; 95% CI: 0.45–0.93). The ZINB model confirmed the adequacy of the approach (alpha: p\u0026lt;0.001; zero-inflation term: p\u0026lt;0.001). In the count process, age was a significant predictor: individuals aged 80 years or older had a 20% lower expected discrimination rate (IRR = 0.80; 95% CI: 0.67–0.97) compared to those aged 50–59 years.\u003c/p\u003e\n\u003cp\u003eConclusions: These findings support the \"Paradox of Perceived Discrimination\", indicating that reports of unfair treatment tend to decline with advancing age despite increased objective vulnerability. This trend potentially reflects psychosocial self-protection mechanisms and internalized age-related beliefs, suggesting the need for intersectional approaches in health policies.\u003c/p\u003e","manuscriptTitle":"The Paradox of Perceived Discrimination in Later Life: Evidence from a Nationally Representative Study in Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 17:11:13","doi":"10.21203/rs.3.rs-8866149/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-29T05:57:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T01:44:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-18T01:49:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68632566033103239765878625403242309382","date":"2026-03-02T10:54:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70719621820820368365001114130575595197","date":"2026-02-26T14:04:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T18:17:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"83816998968905202214251291669690672183","date":"2026-02-24T13:47:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T12:28:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-16T11:20:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-14T01:39:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-14T01:39:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-02-13T00:10:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d202c2fe-48a9-4b6c-bdd5-173ee1398216","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-29T06:09:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 17:11:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8866149","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8866149","identity":"rs-8866149","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}