Care-mediated inequalities in neonatal vitality: a population-based record linkage study in southern Brazil

Care-mediated inequalities in neonatal vitality: a population-based record linkage study in southern Brazil | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Care-mediated inequalities in neonatal vitality: a population-based record linkage study in southern Brazil Silvio Carlos Cury This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9205723/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Persistent inequalities in neonatal outcomes in middle-income settings raise questions about the role of healthcare systems in shaping health disparities. Beyond population characteristics, differences in how obstetric care is delivered may contribute to unequal outcomes. Methods We conducted a population-based analytical study using deterministic record linkage between the Brazilian Live Birth Information System (SINASC) and the Mortality Information System (SIM) from 2017 to 2024 in a municipality in southern Brazil. Neonatal vitality was assessed using low Apgar score at 1 minute (≤ 7) and neonatal mortality. Maternal race (white vs. non-white), mode of delivery, and obstetric context (Robson classification aggregated into macro-groups) were analyzed. Logistic regression models estimated adjusted associations and tested interaction effects. Results The study included 32,650 live births and 157 neonatal deaths. Prematurity (OR ≈ 4.3) and low birth weight (OR ≈ 12.9) were the strongest predictors of adverse outcomes. Vaginal delivery was associated with higher odds of low Apgar score than cesarean section (OR ≈ 1.48; 95% CI 1.06–2.06). Maternal race showed a positive but non-significant association after adjustment. Significant interactions were observed between obstetric context and mode of delivery (p = 0.003), and between mode of delivery and maternal race (p < 0.001), indicating that disparities vary across care pathways. The three-way interaction was not statistically significant. Conclusions Neonatal inequalities are structured through care-mediated pathways in which clinical decisions interact with social position. These findings suggest that improving equity in perinatal health requires not only expanding access but also addressing how care is delivered and how decisions are made within obstetric settings. Neonatal vitality Health inequalities Health equity Obstetric care Mode of delivery Robson classification Brazil Health systems Figures Figure 1 Introduction Inequalities in perinatal health remain a major global public health challenge, particularly in middle-income countries where rapid expansion of healthcare access coexists with persistent disparities in outcomes 1 . Despite substantial improvements in maternal and neonatal indicators over recent decades, marked differences in neonatal vitality continue to be observed across social and racial groups. These disparities are not only epidemiologically relevant but also raise fundamental questions about how health systems function and how care is delivered across different population groups. A growing body of literature has documented consistent associations between maternal race and adverse neonatal outcomes, including low Apgar scores, prematurity, and neonatal mortality 2 , 3 . However, most epidemiological studies conceptualize these inequalities as fixed differences between population groups, often adjusting for clinical and socioeconomic covariates without explicitly addressing the role of healthcare delivery processes. This approach may inadvertently obscure the mechanisms through which inequalities are produced, reproduced, or potentially mitigated within health systems. In obstetric care, the mode of delivery represents a critical and highly consequential decision point. While clinical indications such as fetal distress, previous cesarean section, or maternal complications are important determinants, decisions regarding the timing and mode of delivery are also shaped by institutional norms, provider preferences, availability of resources, and broader organizational cultures. In countries such as Brazil, where cesarean section rates are among the highest in the world 4 , 5 , these decisions acquire additional relevance as potential mediators of both clinical outcomes and social inequalities. The Robson classification provides a widely accepted framework for analyzing obstetric populations and evaluating patterns of care⁶. By categorizing pregnancies into mutually exclusive and clinically meaningful groups based on parity, gestational age, fetal presentation, number of fetuses, and onset of labor, the Robson system allows for standardized comparisons of obstetric practices. Importantly, it enables examination of whether variations in outcomes are attributable to differences in population risk profiles or to differences in care delivery within comparable clinical contexts. Integrating the Robson classification with variables related to mode of delivery and maternal race offers a unique opportunity to explore whether inequalities in neonatal vitality are structured through interactions between social position and clinical decision-making. Such an approach moves beyond traditional risk-factor models and toward a more dynamic understanding of how inequalities are produced within care pathways 7 . In this framework, mode of delivery can be conceptualized not only as a clinical intervention but also as a potential mediator linking social determinants to health outcomes. This perspective aligns with theoretical frameworks that emphasize the role of healthcare systems in shaping health inequalities 7 . Concepts such as structural inequality and institutional bias highlight how social and clinical factors intersect within care pathways, influencing both the quality and outcomes of care. We hypothesize that inequalities in neonatal vitality are not uniformly distributed but are instead structured through care-mediated pathways, particularly those involving decisions about mode of delivery. By testing interaction effects between obstetric context, delivery mode, and race, this study aims to provide empirical evidence on the mechanisms through which inequalities are expressed within healthcare systems. Ultimately, a better understanding of these processes may contribute to the development of more effective and equitable strategies for improving perinatal outcomes, particularly in settings characterized by high intervention rates and persistent social disparities. This conceptual framework is illustrated in Fig. 1 , which outlines how social position, obstetric context, and delivery practices interact to shape neonatal vitality. Methods Study setting Foz do Iguaçu is a medium-sized municipality located in Southern Brazil, in the state of Paraná, at the triple border with Argentina and Paraguay. According to official data from the Instituto Paranaense de Desenvolvimento Econômico e Social (IPARDES), the municipality had an estimated population of approximately 297,000 inhabitants in 2025 8 , with a high level of urbanization and a population density of about 491 inhabitants per square kilometer. Demographic indicators show a predominantly urban population, with a balanced sex distribution and a heterogeneous age structure. The municipality is influenced by intense cross-border mobility and socioeconomic diversity, factors that may affect access to and utilization of healthcare services. Healthcare is provided through the Brazilian Unified Health System (SUS), which ensures universal access to maternal and neonatal care. However, as in other Brazilian settings, high rates of obstetric interventions coexist with persistent inequalities in health outcomes. This context makes Foz do Iguaçu particularly suitable for investigating how healthcare delivery processes may contribute to the production of perinatal inequalities. Study design and data sources This population-based analytical study was conducted using deterministic record linkage between two national health information systems in Brazil: the Live Birth Information System (SINASC) and the Mortality Information System (SIM). The study included data from 2017 to 2024 and was restricted to births and deaths among residents of Foz do Iguaçu, a medium-sized municipality in southern Brazil. SINASC provides detailed information on live births, including maternal characteristics, pregnancy conditions, and neonatal variables, while SIM contains information on deaths, including causes and demographic characteristics. The use of these systems allows for comprehensive population-level analyses of perinatal outcomes 9 . Record linkage procedure Deterministic linkage was performed using a combination of key variables available in both datasets, including date of birth, newborn sex, birth weight, gestational age, and maternal age. This approach prioritizes high specificity in records matching, reducing the likelihood of false-positive matches. However, deterministic linkage may lead to incomplete matching when data quality is imperfect or when there are inconsistencies across records. As such, the linkage strategy adopted in this study is expected to underestimate the total number of matched records, potentially resulting in conservative estimates of neonatal mortality. This trade-off was considered acceptable given the emphasis on internal validity. Study population The initial dataset included all live births recorded in SINASC for the study period. Records with missing or implausible values for key variables—such as gestational age, birth weight, maternal age, and Apgar score—were excluded from the analytical dataset. The final analytical sample consisted of live births with complete information on the outcome and all covariates included in the regression models (complete-case analysis). While this approach may reduce sample size, it ensures consistency across analyses and minimizes bias related to differential missingness in multivariable models. Outcome variables Two outcome variables were considered: Low Apgar score at 1 minute , defined as a score ≤ 7, used as a proxy for compromised neonatal vitality at birth. Neonatal mortality , defined as death occurring within the first 27 days of life. The primary focus of the interaction analyses was on low Apgar score, given its higher frequency and greater statistical power for detecting effect modification. Exposure and covariates The main exposure variable was maternal race or skin color, categorized according to official Brazilian classifications and dichotomized as: White (reference category) Non-white (including Black and mixed-race women) Mode of delivery was categorized as: Vaginal delivery Cesarean section Key clinical covariates included: Gestational age , categorized as preterm (< 37 weeks) or term (≥ 37 weeks) Birth weight , categorized as low birth weight (< 2500 g) 10 or normal (≥ 2500 g) Maternal age , categorized as < 20, 20–34, and ≥ 35 years Obstetric classification Obstetric context was defined using the Robson classification, a standardized system that categorizes all deliveries into ten mutually exclusive and totally inclusive groups based on obstetric characteristics. For analytical purposes, the Robson groups were aggregated into four macro-groups representing different clinical contexts: Low-risk spontaneous labor (Robson groups 1 and 3) Induced labor or pre-labor cesarean without previous uterine scar (groups 2 and 4) Previous cesarean section (group 5) High-complexity pregnancies (groups 6–10) This aggregation allows for a more parsimonious analysis while preserving clinically meaningful distinctions. Statistical analysis The analytical strategy consisted of three main steps: Descriptive analysis , including frequency distributions of key variables and estimation of outcome prevalence across strata defined by obstetric context, mode of delivery, and maternal race. Stratified analysis , in which risks of low Apgar score were calculated within combinations of Robson macro-groups, delivery mode, and race. This step aimed to identify patterns of heterogeneity in neonatal outcomes. Multivariable analysis , using logistic regression models to estimate associations between exposure variables and outcomes. To assess whether these associations varied across clinical contexts, interaction terms were included in the models. Specifically, second-order interactions (Robson × delivery mode; delivery mode × race) and a third-order interaction (Robson × delivery mode × race) were tested. Likelihood ratio tests (LRT) were used to compare nested models and evaluate the statistical significance of interaction terms. A p-value < 0.05 was considered indicative of statistical significance. Adjusted odds ratios (OR) and 95% confidence intervals (95% CI) were estimated for all models. Analytical considerations The use of interaction models reflects a conceptual approach in which inequalities are understood as context-dependent and potentially mediated by healthcare processes. Rather than assuming uniform effects across populations, this strategy allows for the identification of specific clinical contexts in which disparities are more pronounced. Software All data management, linkage procedures, and statistical analyses were performed using R statistical software. Results Study population The analytical sample included 32,650 live births, among which 157 neonatal deaths were identified, corresponding to a neonatal mortality rate of approximately 4.8 per 1,000 live births. The distribution of key variables was consistent with expected patterns in middle-income settings, with a predominance of term births and a substantial proportion of cesarean deliveries (Table 1 ). Table 1 Characteristics of the study population Variable n % Neonatal deaths, n (%) Total births 32,650 100.0 157 (0.48%) Preterm (< 37 weeks) 3,560 10.9 114 (3.20%) Term (≥ 37 weeks) 29,090 89.1 43 (0.15%) Low birth weight (< 2500 g) 2,751 8.4 118 (4.30%) Normal (≥ 2500 g) 29,899 91.6 39 (0.13%) Vaginal 14,213 43.5 70 (0.49%) Cesarean 18,216 55.8 87 (0.48%) White 18,852 57.7 77 (0.41%) Non-white 13,718 42.0 80 (0.58%) Note: Percentages may not sum to 100 due to rounding. Neonatal death defined as death within the first 27 days of life. Prematurity and low birth weight were strongly associated with adverse outcomes. Among preterm newborns, neonatal mortality was markedly higher than among term births. Similarly, low birth weight infants showed a disproportionately higher frequency of both low Apgar scores and neonatal death, confirming their central role as proximal determinants of neonatal risk. Distribution across obstetric context The distribution of births across Robson macro-groups reflected a predominance of lower-risk obstetric profiles, particularly spontaneous labor groups, and an increasing contribution of women with previous cesarean sections. However, adverse neonatal outcomes were not uniformly distributed across these groups. Higher frequencies of low Apgar scores were observed in the high-complexity macro-group , as expected given the underlying clinical risk. In contrast, lower-risk groups showed reduced baseline frequencies but still exhibited variability depending on mode of delivery and maternal characteristics. Stratified analysis: heterogeneity of risks Stratified analyses revealed substantial heterogeneity in neonatal vitality across combinations of obstetric context, mode of delivery, and maternal race, indicating that these differences are context-dependent (Table 2 ). Table 2 Adjusted logistic regression model for low Apgar score (≤ 7) Variable OR 95% CI p-value Low birth weight 12.93 7.94–21.31 < 0.001 Prematurity 4.33 2.70–7.06 < 0.001 Vaginal delivery 1.48 1.06–2.06 0.02 Maternal age ≥ 35 years 1.82 0.99–3.50 0.05 Maternal age 20–34 years 1.38 0.82–2.51 0.21 Non-white (vs white) 1.24 0.90–1.72 0.15 Note : OR = odds ratio; CI = confidence interval. Model adjusted for prematurity, birth weight, maternal age, delivery mode, and maternal race. Reference categories: cesarean delivery, white race, and maternal age < 20 years. In the high-complexity group , cesarean deliveries among non-white mothers were associated with higher proportions of low Apgar scores than white mothers (approximately 24.5% vs. 21.7%). In vaginal deliveries within the same group, patterns were less consistent, suggesting that the effect of delivery mode varies according to clinical context. In lower-risk spontaneous groups , absolute risks were substantially lower, but relative differences persisted. Among cesarean deliveries, non-white mothers consistently showed slightly higher risks than white mothers. In vaginal deliveries, differences were smaller and in some cases reversed, indicating that the relationship between race and neonatal vitality is not uniform across delivery modes (Table 2 ). In the intervention-related groups , disparities appeared more pronounced in cesarean deliveries, with higher risks observed among non-white mothers. This pattern suggests that contexts involving greater clinical discretion or intervention may be particularly relevant for the expression of inequalities. Overall, these findings indicate that neonatal outcomes are shaped by a complex interplay between obstetric context, delivery mode, and maternal race, rather than by isolated effects of individual factors. Multivariable analysis Multivariable logistic regression models (Table 3 ) confirmed the strong association between clinical risk factors and adverse neonatal outcomes, particularly prematurity and low birth weight. Prematurity was associated with an adjusted odds ratio (OR) of approximately 4.3 , while low birth weight showed an even stronger association (OR ≈ 12.9 ), highlighting their dominant role in determining neonatal vitality. Table 3 Stratified risk of low Apgar score (≤ 7) according to obstetric context, delivery mode, and maternal race Robson macro-group Delivery Race n Risk (%) High complexity Cesarean White 1,968 22.0 High complexity Cesarean Non-white 1,401 25.0 High complexity Vaginal White 653 25.0 High complexity Vaginal Non-white 698 20.0 Intervention Cesarean White 4,106 8.0 Intervention Cesarean Non-white 1,735 10.0 Low risk Vaginal White 2,651 6.0 Low risk Vaginal Non-white 2,686 5.0 Note: Risk defined as the percentage of newborns with Apgar score ≤7 at 1 minute. Values rounded to one decimal place. Robson macro-groups represent aggregated obstetric categories. After adjustment for these factors, mode of delivery remained significantly associated with the outcome (Table 3 ). Vaginal delivery was associated with higher odds of low Apgar score than cesarean section (OR ≈ 1.48, 95% CI 1.06–2.06). Maternal race showed a positive but not statistically significant association in the main effects model (OR ≈ 1.24, 95% CI including the null value). Interaction analysis To explore whether these associations varied across clinical contexts, interaction terms were introduced into the models. A significant interaction between obstetric context (Robson macro-group) and mode of delivery was identified (likelihood ratio test, p = 0.003), indicating that the effect of delivery mode on neonatal vitality differs according to the underlying clinical scenario. A second significant interaction was observed between mode of delivery and maternal race (p < 0.001), suggesting that the association between race and neonatal outcomes is modified by how care is delivered. Specifically, disparities were more evident in cesarean deliveries than in vaginal births. In contrast, the three-way interaction between obstetric context, delivery mode, and race was not statistically significant (p = 0.58). This finding indicates that second-order interactions are sufficient to explain most of the variability observed in the data, and that more complex interaction structures do not substantially improve model fit. Synthesis of findings Taken together, the results indicate that neonatal inequalities are not uniformly distributed but are structured through interactions between clinical context and care practices. While biological factors such as prematurity and low birth weight remain the primary determinants of risk, the way care is delivered—particularly decisions regarding mode of delivery—plays a significant role in shaping how these risks are expressed across population groups. Discussion The present study provides evidence that inequalities in neonatal vitality are not uniformly distributed but are structured through interactions between obstetric context, mode of delivery, and maternal race. These factors intersect within healthcare processes, suggesting that disparities are, at least in part, produced through the delivery of care. A central finding is the significant interaction between obstetric context and mode of delivery, indicating that the effect of delivery mode varies according to the underlying clinical scenario. In high-complexity pregnancies, where baseline risk is elevated, the choice of delivery mode may reflect both clinical necessity and system-level constraints. In lower-risk contexts, greater clinical discretion may allow non-clinical factors to influence decision-making, potentially amplifying inequalities. The interaction between mode of delivery and maternal race further suggests that disparities are mediated through how care is delivered. Racial differences were more pronounced in cesarean deliveries than in vaginal births, raising important questions about the processes underlying clinical decision-making. While often life-saving, cesarean section is also subject to variation driven by provider preferences, institutional protocols, and systemic factors, and may function as a mechanism through which social inequalities are translated into unequal outcomes. The absence of a statistically significant three-way interaction suggests that second-order interactions are sufficient to explain most of the observed variability, supporting a model in which inequalities are structured through identifiable and potentially modifiable pathways. These findings align with perspectives in health systems research that emphasize the role of healthcare delivery in shaping inequalities. Rather than acting as neutral intermediaries, healthcare systems can contribute to the production of disparities¹¹ when clinical decision-making is influenced by institutional norms, implicit biases, or unequal access to high-quality care. In obstetric care, these dynamics may be particularly pronounced. Decisions regarding mode of delivery are often made under conditions of uncertainty and varying degrees of standardization, allowing provider- and system-level factors to influence care. Differences in communication, risk perception, and thresholds for intervention may contribute to unequal treatment patterns across social groups. These findings also contribute to the broader debate on health system performance in middle-income settings, where high intervention rates coexist with persistent inequalities. The Brazilian obstetric model exemplifies this paradox 4 , 5 , combining advanced technological capacity with substantial variation in care practices. The results suggest that improving access alone is insufficient; attention must also be directed toward the quality, consistency, and equity of care delivery. From a conceptual standpoint, the study supports a framework of care-mediated inequality, in which disparities emerge from interactions between social position and healthcare processes. This interpretation is consistent with the conceptual framework presented in Fig. 1 . By demonstrating that the effect of race is modified by mode of delivery, the findings highlight the importance of examining how social and clinical factors intersect within specific points of care. The policy implications are substantial. Efforts to reduce perinatal inequalities should extend beyond expanding access and focus on improving how care is delivered, including strengthening clinical governance, promoting adherence to evidence-based guidelines, and reducing unwarranted variation in obstetric practices. Standardizing decision-making around mode of delivery may help mitigate the influence of non-clinical factors. Monitoring systems should incorporate stratified indicators that capture variations across obstetric contexts and social groups. The integration of Robson classification with equity-focused analyses offers a practical approach for identifying priority areas for intervention and informing targeted quality improvement strategies. Some limitations should be acknowledged. Deterministic linkage may have resulted in incomplete matching, potentially underestimating neonatal deaths. The use of administrative data may introduce measurement error and missing data, although the large sample size and population-based design strengthen the robustness of the findings. The use of low Apgar score as a proxy for neonatal vitality does not capture all dimensions of neonatal health but remains a clinically meaningful indicator. Finally, causal interpretations should be made with caution due to potential unmeasured confounding. Despite these limitations, the study demonstrates that inequalities in neonatal outcomes are structured through healthcare processes. By integrating epidemiological analysis with a health systems perspective, it advances understanding of how disparities are produced and identifies actionable points for intervention. Conclusions This study demonstrates that neonatal inequalities are not uniformly distributed but vary according to obstetric context, mode of delivery, and maternal race. While prematurity and low birth weight remain the primary determinants of neonatal risk, the findings indicate that healthcare delivery processes—particularly decisions regarding mode of delivery—play a key role in shaping how these risks are expressed across social groups. The results indicate that disparities are not constant but emerge through specific care pathways, with differences between racial groups becoming more pronounced in contexts involving greater clinical intervention, such as cesarean delivery. This pattern suggests that healthcare systems do not act as neutral intermediaries but as active sites where inequalities are produced and potentially modified. From a policy perspective, reducing perinatal inequalities requires not only expanding access to services but also improving the consistency, quality, and equity of clinical decision-making. Strategies aimed at reducing unwarranted variation in obstetric practices—particularly in decisions related to mode of delivery—may represent a critical pathway toward more equitable neonatal outcomes. Declarations Ethics approval and consent to participate This study used secondary, de-identified data from publicly available health information systems (SINASC and SIM). According to national regulations, studies based exclusively on publicly available, anonymized data do not require formal ethics committee approval. Consent for publication Not applicable. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution S.C.C. conceived the study, designed the research, performed data analysis, and wrote the main manuscript text. S.C.C. also prepared all tables and Figure 1 and reviewed the final version of the manuscript. Data Availability The data used in this study were obtained from the Brazilian Ministry of Health information systems, including the Live Birth Information System (SINASC) and the Mortality Information System (SIM). These data are publicly available through the DATASUS platform (https://datasus.saude.gov.br/), although access to microdata may require specific requests depending on data availability and local regulations. The datasets analyzed during the current study are available from the corresponding author upon reasonable request. References World Health Organization. WHO recommendations on maternal and newborn care for a positive postnatal experience. Geneva: WHO. 2022. Available from: https://www.who.int/publications/i/item/9789240044074 Victora CG, Aquino EML, do Carmo Leal M, Monteiro CA, Barros FC, Szwarcwald CL. Maternal and child health in Brazil: progress and challenges. Lancet. 2011;377(9780):1863–76. https://doi.org/10.1016/S0140-6736(11)60138-4 . Leal MC, Bittencourt SDA, Esteves-Pereira AP, et al. Progress in childbirth care in Brazil: preliminary results of two national studies. Cad Saude Publica. 2019;35(7):e00223018. https://doi.org/10.1590/0102-311X00223018 . Boerma T, Ronsmans C, Melesse DY, et al. Global epidemiology of use of and disparities in caesarean sections. Lancet. 2018;392(10155):1341–8. https://doi.org/10.1016/S0140-6736(18)31928-7 . Betrán AP, Ye J, Moller AB, Zhang J, Gülmezoglu AM, Torloni MR. The increasing trend in caesarean section rates. PLoS ONE. 2016;11(2):e0148343. https://doi.org/10.1371/journal.pone.0148343 . Robson MS. Classification of caesarean sections. Fetal Matern Med Rev. 2001;12(1):23–39. https://doi.org/10.1017/S0965539501000122 . Souza JP, Gülmezoglu AM, Vogel J, et al. Moving beyond essential interventions for reduction of maternal mortality. Lancet. 2013;381(9879):1747–55. https://doi.org/10.1016/S0140-6736(13)60686-8 . Instituto Paranaense de Desenvolvimento Econômico e Social (IPARDES). Cadernos Municipais: Foz do Iguaçu. Curitiba: IPARDES. 2025. Available from: http://www.ipardes.gov.br/ . Accessed 20 Mar 2026. Lawn JE, Blencowe H, Oza S, et al. Every Newborn: progress, priorities, and potential beyond survival. Lancet. 2014;384(9938):189–205. https://doi.org/10.1016/S0140-6736(14)60496-7 . Kramer MS. Determinants of low birth weight: methodological assessment and meta-analysis. Bull World Health Organ. 1987;65(5):663–737. Filippi V, Chou D, Ronsmans C, Graham W. Levels and causes of maternal mortality and morbidity. Reprod Health. 2016;13(1):76. https://doi.org/10.1186/s12978-016-0171-1 . Additional Declarations No competing interests reported. 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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-9205723","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622961614,"identity":"1bd7372d-d640-442d-a84a-def442f0a60c","order_by":0,"name":"Silvio Carlos Cury","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYPACGyBmbDxAipY0kJYGkrQcBpPEaeGfkftM8kfNebu17YeBttTYRBPUInEj3Uya59jt5G1nEoFajqXlNhDSYiCRxibN2HA72ewAUAtjw2HitEj+bDiXbHb+IQlaJHgbDtiZ3SDWFokzz5iteY4lJ5jdANqSQIxf+NvTGG/+qLGzNzuf/vDBhxobwlqAgEUCSCSCVSYQoRwEmD8ACXsiFY+CUTAKRsFIBADisUMzNdj/fwAAAABJRU5ErkJggg==","orcid":"","institution":"Municipal Health Department of Foz do Iguaçu","correspondingAuthor":true,"prefix":"","firstName":"Silvio","middleName":"Carlos","lastName":"Cury","suffix":""}],"badges":[],"createdAt":"2026-03-24 02:27:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9205723/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9205723/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106966972,"identity":"4e0137f9-7f06-47c2-90cc-423930f79834","added_by":"auto","created_at":"2026-04-15 10:02:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71121,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual framework of care-mediated inequality in neonatal vitality.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e The framework illustrates how social position (maternal race), obstetric context, and delivery practices interact to influence neonatal outcomes. Healthcare processes, particularly clinical decision-making, are conceptualized as key mediators through which social inequalities may be expressed in neonatal vitality.\u003c/p\u003e\n\u003cp\u003eSource: Authors’ elaboration.\u003c/p\u003e","description":"","filename":"Figure1IJEH.png","url":"https://assets-eu.researchsquare.com/files/rs-9205723/v1/665d1fef88b612427fd55377.png"},{"id":106994295,"identity":"eeb98fc8-1097-424b-a6df-ec97f30010e3","added_by":"auto","created_at":"2026-04-15 15:07:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1010260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9205723/v1/48c2e716-8ed0-44f2-8af9-24d388ee1968.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Care-mediated inequalities in neonatal vitality: a population-based record linkage study in southern Brazil","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInequalities in perinatal health remain a major global public health challenge, particularly in middle-income countries where rapid expansion of healthcare access coexists with persistent disparities in outcomes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Despite substantial improvements in maternal and neonatal indicators over recent decades, marked differences in neonatal vitality continue to be observed across social and racial groups. These disparities are not only epidemiologically relevant but also raise fundamental questions about how health systems function and how care is delivered across different population groups.\u003c/p\u003e \u003cp\u003eA growing body of literature has documented consistent associations between maternal race and adverse neonatal outcomes, including low Apgar scores, prematurity, and neonatal mortality\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, most epidemiological studies conceptualize these inequalities as fixed differences between population groups, often adjusting for clinical and socioeconomic covariates without explicitly addressing the role of healthcare delivery processes. This approach may inadvertently obscure the mechanisms through which inequalities are produced, reproduced, or potentially mitigated within health systems.\u003c/p\u003e \u003cp\u003eIn obstetric care, the mode of delivery represents a critical and highly consequential decision point. While clinical indications such as fetal distress, previous cesarean section, or maternal complications are important determinants, decisions regarding the timing and mode of delivery are also shaped by institutional norms, provider preferences, availability of resources, and broader organizational cultures. In countries such as Brazil, where cesarean section rates are among the highest in the world\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, these decisions acquire additional relevance as potential mediators of both clinical outcomes and social inequalities. The Robson classification provides a widely accepted framework for analyzing obstetric populations and evaluating patterns of care⁶. By categorizing pregnancies into mutually exclusive and clinically meaningful groups based on parity, gestational age, fetal presentation, number of fetuses, and onset of labor, the Robson system allows for standardized comparisons of obstetric practices. Importantly, it enables examination of whether variations in outcomes are attributable to differences in population risk profiles or to differences in care delivery within comparable clinical contexts.\u003c/p\u003e \u003cp\u003eIntegrating the Robson classification with variables related to mode of delivery and maternal race offers a unique opportunity to explore whether inequalities in neonatal vitality are structured through interactions between social position and clinical decision-making. Such an approach moves beyond traditional risk-factor models and toward a more dynamic understanding of how inequalities are produced within care pathways\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In this framework, mode of delivery can be conceptualized not only as a clinical intervention but also as a potential mediator linking social determinants to health outcomes.\u003c/p\u003e \u003cp\u003eThis perspective aligns with theoretical frameworks that emphasize the role of healthcare systems in shaping health inequalities\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Concepts such as structural inequality and institutional bias highlight how social and clinical factors intersect within care pathways, influencing both the quality and outcomes of care. We hypothesize that inequalities in neonatal vitality are not uniformly distributed but are instead structured through care-mediated pathways, particularly those involving decisions about mode of delivery. By testing interaction effects between obstetric context, delivery mode, and race, this study aims to provide empirical evidence on the mechanisms through which inequalities are expressed within healthcare systems.\u003c/p\u003e \u003cp\u003eUltimately, a better understanding of these processes may contribute to the development of more effective and equitable strategies for improving perinatal outcomes, particularly in settings characterized by high intervention rates and persistent social disparities.\u003c/p\u003e \u003cp\u003eThis conceptual framework is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which outlines how social position, obstetric context, and delivery practices interact to shape neonatal vitality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting\u003c/h2\u003e \u003cp\u003eFoz do Igua\u0026ccedil;u is a medium-sized municipality located in Southern Brazil, in the state of Paran\u0026aacute;, at the triple border with Argentina and Paraguay. According to official data from the Instituto Paranaense de Desenvolvimento Econ\u0026ocirc;mico e Social (IPARDES), the municipality had an estimated population of approximately 297,000 inhabitants in 2025\u003csup\u003e8\u003c/sup\u003e, with a high level of urbanization and a population density of about 491 inhabitants per square kilometer.\u003c/p\u003e \u003cp\u003eDemographic indicators show a predominantly urban population, with a balanced sex distribution and a heterogeneous age structure. The municipality is influenced by intense cross-border mobility and socioeconomic diversity, factors that may affect access to and utilization of healthcare services.\u003c/p\u003e \u003cp\u003eHealthcare is provided through the Brazilian Unified Health System (SUS), which ensures universal access to maternal and neonatal care. However, as in other Brazilian settings, high rates of obstetric interventions coexist with persistent inequalities in health outcomes. This context makes Foz do Igua\u0026ccedil;u particularly suitable for investigating how healthcare delivery processes may contribute to the production of perinatal inequalities.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design and data sources\u003c/h3\u003e\n\u003cp\u003eThis population-based analytical study was conducted using deterministic record linkage between two national health information systems in Brazil: the Live Birth Information System (SINASC) and the Mortality Information System (SIM). The study included data from 2017 to 2024 and was restricted to births and deaths among residents of Foz do Igua\u0026ccedil;u, a medium-sized municipality in southern Brazil.\u003c/p\u003e \u003cp\u003eSINASC provides detailed information on live births, including maternal characteristics, pregnancy conditions, and neonatal variables, while SIM contains information on deaths, including causes and demographic characteristics. The use of these systems allows for comprehensive population-level analyses of perinatal outcomes\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eRecord linkage procedure\u003c/h3\u003e\n\u003cp\u003eDeterministic linkage was performed using a combination of key variables available in both datasets, including date of birth, newborn sex, birth weight, gestational age, and maternal age. This approach prioritizes high specificity in records matching, reducing the likelihood of false-positive matches.\u003c/p\u003e \u003cp\u003eHowever, deterministic linkage may lead to incomplete matching when data quality is imperfect or when there are inconsistencies across records. As such, the linkage strategy adopted in this study is expected to underestimate the total number of matched records, potentially resulting in conservative estimates of neonatal mortality. This trade-off was considered acceptable given the emphasis on internal validity.\u003c/p\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe initial dataset included all live births recorded in SINASC for the study period. Records with missing or implausible values for key variables\u0026mdash;such as gestational age, birth weight, maternal age, and Apgar score\u0026mdash;were excluded from the analytical dataset.\u003c/p\u003e \u003cp\u003eThe final analytical sample consisted of live births with complete information on the outcome and all covariates included in the regression models (complete-case analysis). While this approach may reduce sample size, it ensures consistency across analyses and minimizes bias related to differential missingness in multivariable models.\u003c/p\u003e\n\u003ch3\u003eOutcome variables\u003c/h3\u003e\n\u003cp\u003eTwo outcome variables were considered:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLow Apgar score at 1 minute\u003c/b\u003e, defined as a score\u0026thinsp;\u0026le;\u0026thinsp;7, used as a proxy for compromised neonatal vitality at birth.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNeonatal mortality\u003c/b\u003e, defined as death occurring within the first 27 days of life.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe primary focus of the interaction analyses was on low Apgar score, given its higher frequency and greater statistical power for detecting effect modification.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExposure and covariates\u003c/h2\u003e \u003cp\u003eThe main exposure variable was maternal race or skin color, categorized according to official Brazilian classifications and dichotomized as:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eWhite (reference category)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNon-white (including Black and mixed-race women)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eMode of delivery was categorized as:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eVaginal delivery\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCesarean section\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eKey clinical covariates included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGestational age\u003c/b\u003e, categorized as preterm (\u0026lt;\u0026thinsp;37 weeks) or term (\u0026ge;\u0026thinsp;37 weeks)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBirth weight\u003c/b\u003e, categorized as low birth weight (\u0026lt;\u0026thinsp;2500 g)\u003csup\u003e10\u003c/sup\u003e or normal (\u0026ge;\u0026thinsp;2500 g)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMaternal age\u003c/b\u003e, categorized as \u0026lt;\u0026thinsp;20, 20\u0026ndash;34, and \u0026ge;\u0026thinsp;35 years\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eObstetric classification\u003c/h3\u003e\n\u003cp\u003eObstetric context was defined using the Robson classification, a standardized system that categorizes all deliveries into ten mutually exclusive and totally inclusive groups based on obstetric characteristics.\u003c/p\u003e \u003cp\u003eFor analytical purposes, the Robson groups were aggregated into four macro-groups representing different clinical contexts:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLow-risk spontaneous labor\u003c/b\u003e (Robson groups 1 and 3)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInduced labor or pre-labor cesarean without previous uterine scar\u003c/b\u003e (groups 2 and 4)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePrevious cesarean section\u003c/b\u003e (group 5)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHigh-complexity pregnancies\u003c/b\u003e (groups 6\u0026ndash;10)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis aggregation allows for a more parsimonious analysis while preserving clinically meaningful distinctions.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe analytical strategy consisted of three main steps:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDescriptive analysis\u003c/b\u003e, including frequency distributions of key variables and estimation of outcome prevalence across strata defined by obstetric context, mode of delivery, and maternal race.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStratified analysis\u003c/b\u003e, in which risks of low Apgar score were calculated within combinations of Robson macro-groups, delivery mode, and race. This step aimed to identify patterns of heterogeneity in neonatal outcomes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMultivariable analysis\u003c/b\u003e, using logistic regression models to estimate associations between exposure variables and outcomes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTo assess whether these associations varied across clinical contexts, interaction terms were included in the models. Specifically, second-order interactions (Robson \u0026times; delivery mode; delivery mode \u0026times; race) and a third-order interaction (Robson \u0026times; delivery mode \u0026times; race) were tested.\u003c/p\u003e \u003cp\u003eLikelihood ratio tests (LRT) were used to compare nested models and evaluate the statistical significance of interaction terms. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered indicative of statistical significance.\u003c/p\u003e \u003cp\u003eAdjusted odds ratios (OR) and 95% confidence intervals (95% CI) were estimated for all models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalytical considerations\u003c/h2\u003e \u003cp\u003eThe use of interaction models reflects a conceptual approach in which inequalities are understood as context-dependent and potentially mediated by healthcare processes. Rather than assuming uniform effects across populations, this strategy allows for the identification of specific clinical contexts in which disparities are more pronounced.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSoftware\u003c/h2\u003e \u003cp\u003eAll data management, linkage procedures, and statistical analyses were performed using R statistical software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003eThe analytical sample included 32,650 live births, among which 157 neonatal deaths were identified, corresponding to a neonatal mortality rate of approximately 4.8 per 1,000 live births. The distribution of key variables was consistent with expected patterns in middle-income settings, with a predominance of term births and a substantial proportion of cesarean deliveries (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of the study population\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eNeonatal deaths, n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal births\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e32,650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e157 (0.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePreterm (\u0026lt;\u0026thinsp;37 weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e3,560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e114 (3.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTerm (\u0026ge;\u0026thinsp;37 weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e29,090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e89.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e43 (0.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLow birth weight (\u0026lt;\u0026thinsp;2500 g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e2,751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e118 (4.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNormal (\u0026ge;\u0026thinsp;2500 g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e29,899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e91.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e39 (0.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVaginal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e14,213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e43.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e70 (0.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCesarean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e18,216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e55.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e87 (0.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e18,852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e57.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e77 (0.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNon-white\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e13,718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e80 (0.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Percentages may not sum to 100 due to rounding. Neonatal death defined as death within the first 27 days of life.\u003c/p\u003e\n \u003cp\u003ePrematurity and low birth weight were strongly associated with adverse outcomes. Among preterm newborns, neonatal mortality was markedly higher than among term births. Similarly, low birth weight infants showed a disproportionately higher frequency of both low Apgar scores and neonatal death, confirming their central role as proximal determinants of neonatal risk.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eDistribution across obstetric context\u003c/h2\u003e\n \u003cp\u003eThe distribution of births across Robson macro-groups reflected a predominance of lower-risk obstetric profiles, particularly spontaneous labor groups, and an increasing contribution of women with previous cesarean sections.\u003c/p\u003e\n \u003cp\u003eHowever, adverse neonatal outcomes were not uniformly distributed across these groups. Higher frequencies of low Apgar scores were observed in the \u003cstrong\u003ehigh-complexity macro-group\u003c/strong\u003e, as expected given the underlying clinical risk. In contrast, lower-risk groups showed reduced baseline frequencies but still exhibited variability depending on mode of delivery and maternal characteristics.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eStratified analysis: heterogeneity of risks\u003c/h2\u003e\n \u003cp\u003eStratified analyses revealed substantial heterogeneity in neonatal vitality across combinations of obstetric context, mode of delivery, and maternal race, indicating that these differences are context-dependent (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdjusted logistic regression model for low Apgar score (\u0026le;\u0026thinsp;7)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLow birth weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e12.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e7.94\u0026ndash;21.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePrematurity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2.70\u0026ndash;7.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVaginal delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1.06\u0026ndash;2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMaternal age\u0026thinsp;\u0026ge;\u0026thinsp;35 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.99\u0026ndash;3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMaternal age 20\u0026ndash;34 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.82\u0026ndash;2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNon-white (vs white)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.90\u0026ndash;1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: OR\u0026thinsp;=\u0026thinsp;odds ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval. Model adjusted for prematurity, birth weight, maternal age, delivery mode, and maternal race. Reference categories: cesarean delivery, white race, and maternal age\u0026thinsp;\u0026lt;\u0026thinsp;20 years.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eIn the \u003cstrong\u003ehigh-complexity group\u003c/strong\u003e, cesarean deliveries among non-white mothers were associated with higher proportions of low Apgar scores than white mothers (approximately 24.5% vs. 21.7%). In vaginal deliveries within the same group, patterns were less consistent, suggesting that the effect of delivery mode varies according to clinical context.\u003c/p\u003e\n \u003cp\u003eIn \u003cstrong\u003elower-risk spontaneous groups\u003c/strong\u003e, absolute risks were substantially lower, but relative differences persisted. Among cesarean deliveries, non-white mothers consistently showed slightly higher risks than white mothers. In vaginal deliveries, differences were smaller and in some cases reversed, indicating that the relationship between race and neonatal vitality is not uniform across delivery modes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn the \u003cstrong\u003eintervention-related groups\u003c/strong\u003e, disparities appeared more pronounced in cesarean deliveries, with higher risks observed among non-white mothers. This pattern suggests that contexts involving greater clinical discretion or intervention may be particularly relevant for the expression of inequalities.\u003c/p\u003e\n \u003cp\u003eOverall, these findings indicate that neonatal outcomes are shaped by a complex interplay between obstetric context, delivery mode, and maternal race, rather than by isolated effects of individual factors.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eMultivariable analysis\u003c/h2\u003e\n \u003cp\u003eMultivariable logistic regression models (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) confirmed the strong association between clinical risk factors and adverse neonatal outcomes, particularly prematurity and low birth weight. Prematurity was associated with an adjusted odds ratio (OR) of approximately \u003cstrong\u003e4.3\u003c/strong\u003e, while low birth weight showed an even stronger association (OR\u0026thinsp;\u0026asymp;\u0026thinsp;\u003cstrong\u003e12.9\u003c/strong\u003e), highlighting their dominant role in determining neonatal vitality.\u0026nbsp;\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStratified risk of low Apgar score (\u0026le;\u0026thinsp;7) according to obstetric context, delivery mode, and maternal race\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRobson macro-group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDelivery\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eRisk (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh complexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCesarean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1,968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh complexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCesarean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNon-white\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1,401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh complexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eVaginal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh complexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eVaginal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNon-white\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIntervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCesarean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e4,106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIntervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCesarean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNon-white\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1,735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eVaginal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2,651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eVaginal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNon-white\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2,686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Risk defined as the percentage of newborns with Apgar score \u0026le;7 at 1 minute. Values rounded to one decimal place. Robson macro-groups represent aggregated obstetric categories.\u003c/p\u003e\n \u003cp\u003eAfter adjustment for these factors, mode of delivery remained significantly associated with the outcome (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Vaginal delivery was associated with higher odds of low Apgar score than cesarean section (OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.48, 95% CI 1.06\u0026ndash;2.06). Maternal race showed a positive but not statistically significant association in the main effects model (OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.24, 95% CI including the null value).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eInteraction analysis\u003c/h2\u003e\n \u003cp\u003eTo explore whether these associations varied across clinical contexts, interaction terms were introduced into the models.\u003c/p\u003e\n \u003cp\u003eA significant interaction between \u003cstrong\u003eobstetric context (Robson macro-group) and mode of delivery\u003c/strong\u003e was identified (likelihood ratio test, p\u0026thinsp;=\u0026thinsp;0.003), indicating that the effect of delivery mode on neonatal vitality differs according to the underlying clinical scenario.\u003c/p\u003e\n \u003cp\u003eA second significant interaction was observed between \u003cstrong\u003emode of delivery and maternal race\u003c/strong\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that the association between race and neonatal outcomes is modified by how care is delivered. Specifically, disparities were more evident in cesarean deliveries than in vaginal births.\u003c/p\u003e\n \u003cp\u003eIn contrast, the \u003cstrong\u003ethree-way interaction\u003c/strong\u003e between obstetric context, delivery mode, and race was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.58). This finding indicates that second-order interactions are sufficient to explain most of the variability observed in the data, and that more complex interaction structures do not substantially improve model fit.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eSynthesis of findings\u003c/h2\u003e\n \u003cp\u003eTaken together, the results indicate that neonatal inequalities are not uniformly distributed but are structured through interactions between clinical context and care practices. While biological factors such as prematurity and low birth weight remain the primary determinants of risk, the way care is delivered\u0026mdash;particularly decisions regarding mode of delivery\u0026mdash;plays a significant role in shaping how these risks are expressed across population groups.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study provides evidence that inequalities in neonatal vitality are not uniformly distributed but are structured through interactions between obstetric context, mode of delivery, and maternal race. These factors intersect within healthcare processes, suggesting that disparities are, at least in part, produced through the delivery of care.\u003c/p\u003e \u003cp\u003eA central finding is the significant interaction between obstetric context and mode of delivery, indicating that the effect of delivery mode varies according to the underlying clinical scenario. In high-complexity pregnancies, where baseline risk is elevated, the choice of delivery mode may reflect both clinical necessity and system-level constraints. In lower-risk contexts, greater clinical discretion may allow non-clinical factors to influence decision-making, potentially amplifying inequalities.\u003c/p\u003e \u003cp\u003eThe interaction between mode of delivery and maternal race further suggests that disparities are mediated through how care is delivered. Racial differences were more pronounced in cesarean deliveries than in vaginal births, raising important questions about the processes underlying clinical decision-making.\u003c/p\u003e \u003cp\u003eWhile often life-saving, cesarean section is also subject to variation driven by provider preferences, institutional protocols, and systemic factors, and may function as a mechanism through which social inequalities are translated into unequal outcomes.\u003c/p\u003e \u003cp\u003eThe absence of a statistically significant three-way interaction suggests that second-order interactions are sufficient to explain most of the observed variability, supporting a model in which inequalities are structured through identifiable and potentially modifiable pathways.\u003c/p\u003e \u003cp\u003eThese findings align with perspectives in health systems research that emphasize the role of healthcare delivery in shaping inequalities. Rather than acting as neutral intermediaries, healthcare systems can contribute to the production of disparities\u0026sup1;\u0026sup1; when clinical decision-making is influenced by institutional norms, implicit biases, or unequal access to high-quality care.\u003c/p\u003e \u003cp\u003eIn obstetric care, these dynamics may be particularly pronounced. Decisions regarding mode of delivery are often made under conditions of uncertainty and varying degrees of standardization, allowing provider- and system-level factors to influence care. Differences in communication, risk perception, and thresholds for intervention may contribute to unequal treatment patterns across social groups.\u003c/p\u003e \u003cp\u003eThese findings also contribute to the broader debate on health system performance in middle-income settings, where high intervention rates coexist with persistent inequalities. The Brazilian obstetric model exemplifies this paradox\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, combining advanced technological capacity with substantial variation in care practices. The results suggest that improving access alone is insufficient; attention must also be directed toward the quality, consistency, and equity of care delivery.\u003c/p\u003e \u003cp\u003eFrom a conceptual standpoint, the study supports a framework of care-mediated inequality, in which disparities emerge from interactions between social position and healthcare processes. This interpretation is consistent with the conceptual framework presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. By demonstrating that the effect of race is modified by mode of delivery, the findings highlight the importance of examining how social and clinical factors intersect within specific points of care.\u003c/p\u003e \u003cp\u003eThe policy implications are substantial. Efforts to reduce perinatal inequalities should extend beyond expanding access and focus on improving how care is delivered, including strengthening clinical governance, promoting adherence to evidence-based guidelines, and reducing unwarranted variation in obstetric practices. Standardizing decision-making around mode of delivery may help mitigate the influence of non-clinical factors.\u003c/p\u003e \u003cp\u003eMonitoring systems should incorporate stratified indicators that capture variations across obstetric contexts and social groups. The integration of Robson classification with equity-focused analyses offers a practical approach for identifying priority areas for intervention and informing targeted quality improvement strategies.\u003c/p\u003e \u003cp\u003eSome limitations should be acknowledged. Deterministic linkage may have resulted in incomplete matching, potentially underestimating neonatal deaths. The use of administrative data may introduce measurement error and missing data, although the large sample size and population-based design strengthen the robustness of the findings. The use of low Apgar score as a proxy for neonatal vitality does not capture all dimensions of neonatal health but remains a clinically meaningful indicator. Finally, causal interpretations should be made with caution due to potential unmeasured confounding.\u003c/p\u003e \u003cp\u003eDespite these limitations, the study demonstrates that inequalities in neonatal outcomes are structured through healthcare processes. By integrating epidemiological analysis with a health systems perspective, it advances understanding of how disparities are produced and identifies actionable points for intervention.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that neonatal inequalities are not uniformly distributed but vary according to obstetric context, mode of delivery, and maternal race. While prematurity and low birth weight remain the primary determinants of neonatal risk, the findings indicate that healthcare delivery processes\u0026mdash;particularly decisions regarding mode of delivery\u0026mdash;play a key role in shaping how these risks are expressed across social groups.\u003c/p\u003e \u003cp\u003eThe results indicate that disparities are not constant but emerge through specific care pathways, with differences between racial groups becoming more pronounced in contexts involving greater clinical intervention, such as cesarean delivery. This pattern suggests that healthcare systems do not act as neutral intermediaries but as active sites where inequalities are produced and potentially modified.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, reducing perinatal inequalities requires not only expanding access to services but also improving the consistency, quality, and equity of clinical decision-making. Strategies aimed at reducing unwarranted variation in obstetric practices\u0026mdash;particularly in decisions related to mode of delivery\u0026mdash;may represent a critical pathway toward more equitable neonatal outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study used secondary, de-identified data from publicly available health information systems (SINASC and SIM). According to national regulations, studies based exclusively on publicly available, anonymized data do not require formal ethics committee approval.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eS.C.C. conceived the study, designed the research, performed data analysis, and wrote the main manuscript text. S.C.C. also prepared all tables and Figure 1 and reviewed the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data used in this study were obtained from the Brazilian Ministry of Health information systems, including the Live Birth Information System (SINASC) and the Mortality Information System (SIM). These data are publicly available through the DATASUS platform (https://datasus.saude.gov.br/), although access to microdata may require specific requests depending on data availability and local regulations. The datasets analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. WHO recommendations on maternal and newborn care for a positive postnatal experience. Geneva: WHO. 2022. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/9789240044074\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/9789240044074\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVictora CG, Aquino EML, do Carmo Leal M, Monteiro CA, Barros FC, Szwarcwald CL. Maternal and child health in Brazil: progress and challenges. Lancet. 2011;377(9780):1863\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(11)60138-4\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(11)60138-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeal MC, Bittencourt SDA, Esteves-Pereira AP, et al. 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Lancet. 2018;392(10155):1341\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(18)31928-7\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(18)31928-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBetr\u0026aacute;n AP, Ye J, Moller AB, Zhang J, G\u0026uuml;lmezoglu AM, Torloni MR. The increasing trend in caesarean section rates. PLoS ONE. 2016;11(2):e0148343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0148343\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0148343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobson MS. Classification of caesarean sections. Fetal Matern Med Rev. 2001;12(1):23\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0965539501000122\u003c/span\u003e\u003cspan address=\"10.1017/S0965539501000122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSouza JP, G\u0026uuml;lmezoglu AM, Vogel J, et al. Moving beyond essential interventions for reduction of maternal mortality. Lancet. 2013;381(9879):1747\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(13)60686-8\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(13)60686-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInstituto Paranaense de Desenvolvimento Econ\u0026ocirc;mico e Social (IPARDES). Cadernos Municipais: Foz do Igua\u0026ccedil;u. Curitiba: IPARDES. 2025. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ipardes.gov.br/\u003c/span\u003e\u003cspan address=\"http://www.ipardes.gov.br/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 20 Mar 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawn JE, Blencowe H, Oza S, et al. Every Newborn: progress, priorities, and potential beyond survival. Lancet. 2014;384(9938):189\u0026ndash;205. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(14)60496-7\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(14)60496-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKramer MS. Determinants of low birth weight: methodological assessment and meta-analysis. Bull World Health Organ. 1987;65(5):663\u0026ndash;737.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFilippi V, Chou D, Ronsmans C, Graham W. Levels and causes of maternal mortality and morbidity. Reprod Health. 2016;13(1):76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12978-016-0171-1\u003c/span\u003e\u003cspan address=\"10.1186/s12978-016-0171-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neonatal vitality, Health inequalities, Health equity, Obstetric care, Mode of delivery, Robson classification, Brazil, Health systems","lastPublishedDoi":"10.21203/rs.3.rs-9205723/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9205723/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePersistent inequalities in neonatal outcomes in middle-income settings raise questions about the role of healthcare systems in shaping health disparities. Beyond population characteristics, differences in how obstetric care is delivered may contribute to unequal outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a population-based analytical study using deterministic record linkage between the Brazilian Live Birth Information System (SINASC) and the Mortality Information System (SIM) from 2017 to 2024 in a municipality in southern Brazil. Neonatal vitality was assessed using low Apgar score at 1 minute (\u0026le;\u0026thinsp;7) and neonatal mortality. Maternal race (white vs. non-white), mode of delivery, and obstetric context (Robson classification aggregated into macro-groups) were analyzed. Logistic regression models estimated adjusted associations and tested interaction effects.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 32,650 live births and 157 neonatal deaths. Prematurity (OR\u0026thinsp;\u0026asymp;\u0026thinsp;4.3) and low birth weight (OR\u0026thinsp;\u0026asymp;\u0026thinsp;12.9) were the strongest predictors of adverse outcomes. Vaginal delivery was associated with higher odds of low Apgar score than cesarean section (OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.48; 95% CI 1.06\u0026ndash;2.06). Maternal race showed a positive but non-significant association after adjustment. Significant interactions were observed between obstetric context and mode of delivery (p\u0026thinsp;=\u0026thinsp;0.003), and between mode of delivery and maternal race (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that disparities vary across care pathways. The three-way interaction was not statistically significant.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eNeonatal inequalities are structured through care-mediated pathways in which clinical decisions interact with social position. These findings suggest that improving equity in perinatal health requires not only expanding access but also addressing how care is delivered and how decisions are made within obstetric settings.\u003c/p\u003e","manuscriptTitle":"Care-mediated inequalities in neonatal vitality: a population-based record linkage study in southern Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-15 09:10:07","doi":"10.21203/rs.3.rs-9205723/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f0c68b5-5c13-4787-b6d5-d5cda52cffb2","owner":[],"postedDate":"April 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T09:10:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-15 09:10:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9205723","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9205723","identity":"rs-9205723","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}