Maria Yury Ichihara

Racial residential segregation constitutes a central dimension of urban inequalities, as it shapes how population groups are distributed across the territory and how they access infrastructure, public services, and opportunities. This article analyzes racial segregation in Salvador’s neighborhoods, comparing the 2010 and 2022 Demographic Censuses of the Brazilian Bureau of Statistics. The analysis uses the dissimilarity index to measure residential distance between Blacks versus Whites, Browns ( pardos ) versus Whites, and Afro-descendants ( negros ) versus Whites, complemented by descriptive statistics and spatial analysis using local indicators of spatial association (LISA) to identify patterns of spatial concentration and population composition. The results show relative stability in average segregation levels between the two censuses, with greater intensity in comparisons between Blacks versus Whites, then between Browns versus Whites, and finally between Afro-descendants versus Whites. At the same time, index dispersion increases and spatial clusters persist in neighborhoods along the Atlantic coast and in central areas, whereas segregation is lower in the city center and the suburbs. The findings indicate that racial segregation continues to structure the urban space of Salvador and suggest the need for locally oriented public policies capable of integrating urban planning, housing, mobility, and service provision from a perspective of racial equity.

Background/Objectives: Well-child care plays a critical role in promoting child health and monitoring growth and development within Primary Health Care (PHC), in line with international frameworks such as the WHO Global Strategy and the UN Sustainable Development Goals (SDGs). However, the absence of standardized quality indicators limits comparability across studies and hinders continuous improvement worldwide. This study aimed to map and analyze the indicators used to assess the quality of well-child care in global PHC settings. Methods: A scoping review was conducted following PRISMA-ScR and Joanna Briggs Institute methodological guidance, with a pre-registered protocol. Comprehensive searches were performed in May 2025 across fourteen databases and two gray literature sources, without language or time restrictions. Eligible studies assessed quality indicators for well-child care among children up to 5 years, 11 months, and 29 days. Two independent reviewers performed study selection and data extraction. Results: From 6052 records, 62 studies met inclusion criteria. Out of them, most (68%) used composite indicators, primarily from pre-existing tools (67%). While structural and clinical indicators—such as immunization and service accessibility—were predominant, there was a critical absence of relational indicators focusing on patient–provider interaction. This lack of standardization and neglect of the relational dimension significantly hinders international comparability and the assessment of family-centered care quality. Conclusions: Developing and validating a core set of standardized, comprehensive, and context-sensitive indicators integrating structural, clinical, and relational dimensions is essential. These should be linked to information systems to enable robust national and international comparison, strengthen evidence-based management, and drive continuous quality improvement to achieve the 2030 Agenda goals. These findings provide a foundation for policymakers to develop standardized monitoring tools that prioritize neglected relational aspects of care.

Background: The COVID-19 pandemic progressed unevenly across the 417 municipalities of Bahia, Brazil. Pinpointing where and when risk peaked is vital for preparing for future emergencies. Methods: We performed an ecological, spatiotemporal study using COVID-19-confirmed cases in Bahia, Brazil, from January 2020 to December 2022. A discrete Poisson space–time scan in SaTScan-identified clusters. For each cluster, we calculated relative risk (RR) and Log Likelihood Ratio, considering p < 0.05 as significant. Results: A total of 33 clusters were detected; 25 statistically significant. The largest cluster (164 municipalities; May 2020–June 2021) comprised 702,720 observed versus 338,822 expected cases (RR = 2.8). Two overlapping large clusters (185 and 136 municipalities) during January–February 2022—coinciding with Omicron circulation—showed RR > 2.0. Localized clusters reached RR > 3.0. Spatially, risk concentrated in the south, southwest, and east of the state, with isolated countryside outbreaks. Conclusions: The heterogeneous spatiotemporal dynamics of COVID-19 in Bahia underscore the value of cluster detection for targeted surveillance and resource allocation. We recommend employing statistical techniques for early detection and control, as well as conducting further studies on socioeconomic and behavioral factors.

Background: The COVID-19 pandemic progressed unevenly across the 417 municipalities of Bahia, Brazil. Pinpointing where and when risk peaked is vital for guiding interventions and preparing for future emergencies. Methods: We performed an ecological spatiotemporal study using confirmed cases recorded in e-SUS Notifica and Sivep-Gripe from January 2020 to December 2022. A discrete Poisson space-time scan in SaTScan (spatial window ≤ 50 % of the population; temporal window ≤ 6 months) identified clusters. For each cluster we calculated relative risk (RR) and log-likelihood ratio, considering p < 0.05 significant. Results: Thirty-three clusters were detected, 25 statistically significant. The largest cluster (164 municipalities; May 2020–June 2021) comprised 702 720 observed versus 338 822 expected cases (RR = 2.8). Two overlapping large clusters (185 and 136 municipalities) during January–February 2022—coinciding with Omicron circulation—showed RR ≈ 2.2. Localised clusters reached RR = 3.37. Spatially, risk concentrated in the south, southwest and east of the state, with isolated contryside outbreaks. Conclusions: The heterogeneous spatiotemporal dynamics of COVID-19 in Bahia underscore the value of real-time cluster detection for targeted surveillance and resource allocation. Tailored strategies, supported by genomic surveillance and socioeconomic indicators, are essential to mitigate future respiratory events.

IntroductionMonitoring and addressing health inequalities is important. However, socioeconomic variables are usually unavailable within health datasets. Area deprivation measures provide access to open-source reliable socioeconomic data within low/middle-income countries and can contribute to the monitoring of the Sustainable Development Goals and assessing the growing burden of health inequalities. ObjectiveTo create a small-area deprivation measure for the whole of Brazil - the Brazilian Deprivation Index (Índice Brasileiro de Privação - IBP). MethodsUsing Census Sector data (mean population size=615) from the most recently available Brazilian Demographic Census (2010), variables measuring literacy, household income and housing conditions were standardised using z-scores and summed into a single measure. The IBP was validated using regional small-area measures of vulnerability: Belo Horizonte's Health Vulnerability Index (IVS) and S\~{a}o Paulo's Social Vulnerability Index (IPVS). Mortality data from Minas Gerais were used to estimate age-standardised mortality rates (ASMR) by ill-defined causes across IBP deprivation quintiles. ResultsThe IBP was created for 303,218 (97.8%) census sectors (99.7% population). Substantial regional variation in deprivation was found using the IBP measure, with higher deprivation in rural than urban areas. The IBP was correlated with the other indicators used for validation: the IVS (r = 0.96) and the IPVS (r = 0.68). We found gradients across the ill-defined causes ASMR, in Minas Gerais mortality was 2.6 higher in the most deprived quintile of IBP, compared with the least deprived. Main challenges in creating a deprivation measure for LMICs and possible solutions are demonstrated. ConclusionA small area deprivation index was created for Brazil, a large and highly diverse middle-income country. The IBP improves our understanding and monitoring of inequalities, serving as a valuable tool for informing targeted public policies. Although the index is based on Brazil's specific context, the challenges faced, and the strategies implemented to tackle them are relevant for other low- and middle-income countries aiming to develop similar tools.

Background: The COVID-19 pandemic had significant impacts on healthcare systems, including the disruption of essential services such as childhood immunization. Containment measures, such as social distancing, contributed to reduced adherence to vaccination programs, increasing the risk of re-emerging vaccine-preventable diseases. We aim to assess the evolution of childhood vaccination coverage in Brazil from 2010 to 2024, identifying trends before, during, and after the COVID-19 pandemic. Methods: An interrupted time series (ITS) study was conducted using publicly available aggregated data on vaccination coverage for children under one year of age. Prais–Winsten regression models were applied to estimate trend changes and evaluate the impact of the pandemic on immunization levels. Results: The findings indicate a progressive decline in vaccination coverage between 2010 and 2019, which was intensified in 2020 by the pandemic. The BCG vaccine showed the greatest decline (−24.88%, p < 0.001), while pentavalent and hepatitis B vaccines decreased annually by −3.72% and −2.21%, respectively. From 2021 onwards, a gradual recovery in coverage was observed, with significant increases for BCG (+7.48% per year, p < 0.001), hepatitis B (+7.45%, p = 0.014), and MMR (+6.73%, p = 0.017) vaccines. Discussion: The results highlight a concerning decline in childhood immunization, exacerbated by the pandemic but showing recent signs of recovery. This scenario underscores structural challenges within the National Immunization Program, requiring coordinated efforts to reverse vaccination losses and ensure system resilience in the face of future crises.

Background: This paper focuses on the impacts of climate change on vulnerable ecosystems and its implications for the health and well-being of populations. It specifically examines the Semi-arid region of Brazil, where the introduction of a social climate adaptation tool, cisterns, has brought about significant positive changes. Cisterns, a low-cost climate adaptation technology, can be replicated globally, reducing the negative health impacts of frequent droughts, especially for vulnerable groups in remote rural areas. Objective: We analyze the impact of the “One Million Cisterns Program” (P1MC) on health by synthesizing the literature and modeling its interactions with climatic and environmental factors with the DPSEEA framework. Methods: Our case study employs a multidisciplinary approach, focusing on two key objectives: (i) synthesizing the literature on the implementation of the P1MC and its association with health outcomes; and (ii) developing a conceptual framework to model the relationship between climatic and environmental factors, adaptive ecosystems, and health outcomes. The Driving Force–Pressure–State–Exposure–Effect–Action (DPSEEA) framework evaluates the structural connections between climate change and human health. Findings: The study found a significant gap in the literature concerning the relationship between P1MC and health outcomes. Cisterns target the pressure/state linkages related to contextual factors and health effects, addressing the root causes of drought-related health issues. This framework also provides a foundation for collaboration among health, environmental, and policy sectors to address shared challenges, such as water security and health outcomes. Conclusion: We offer a multidisciplinary analytical framework that can be used to explore various perspectives – environmental, social, and health-related – with experts and stakeholders to develop and improve adaptive social technology strategies for living in the emergence of climate change. This framework also facilitates the implementation of qualitative and quantitative well-being and health assessments.

Background: Despite adverse events following immunization (AEFI) being well described in vaccine trials, there is a need to produce more real-world data on events supposedly attributed to vaccination against COVID-19. This study aims to estimate the prevalence of AEFI in the first dose of COVID-19 vaccines in the state of Brazil and to verify whether such events differ among the types of vaccines offered in this country. Methods: A population-based study using linked administrative data on vaccine registry and adverse events following immunization in 2021 and 2022. The study included 10,169,378 individuals aged 18 or over who lived in Bahia and received the first dose of COVID-19 vaccines. We calculated AEFI prevalence and verified differences among vaccines by logistic regression to estimate crude and adjusted by sex and age group prevalence ratio (PR). Results: The prevalence of AEFI was 74.3 per 100,000 doses applied, with a higher rate of nonserious events, mainly following the ChAdOx1-S. More than two-thirds of these adverse effects occurred in women, and almost half were between 30 and 49 years old. The individuals who received ChAdOx1-S had a 125% higher prevalence than those who received CoronaVac. Those who received BNT162b2 and Ad26.COV2.S had a 71% and 58%, respectively, lower prevalence of AEFI than those who received CoronaVac. Conclusions: The use of vaccines against COVID-19 has proven to be positive and effective in combating SARS-CoV-2, significantly reducing morbidity and mortality from the disease. We cannot deny the presence of adverse events in the context of vaccination. However, the vaccines have proven to be safe and reliable. The results of this study offer relevant data that can contribute to the qualification of AEFI pharmacovigilance in Brazil and worldwide.

This study aimed to characterize the quality of child health care and explore its relationship with municipal characteristics. Using data from the external assessment of the first cycle of the Brazilian National Program for Access and Quality Improvement in Primary Care (PMAQ-AB, acronym in Portuguese), this cross-sectional study evaluated 16,566 Family Health Strategy teams. In total, nine binary indicators were created based on recommendations from the Brazilian Ministry of Health. We employed latent class analysis and multinomial logistic regression to assess the quality of care and its association with region and the Brazilian Deprivation Index. Three patterns of quality of care were identified: high, intermediate, and low adequacy. The “high adequacy” pattern included 22.5% of teams, “intermediate adequacy” 60.2%, and “low adequacy” only 17.3%. Teams in the Northeast Region were over twice as likely to belong to the “high adequacy” pattern (OR = 2.34; 95%CI: 1.15-4.76) compared with those in the Central-West Region. For teams located in municipalities with moderate and low deprivation, the chance of belonging to the “high adequacy” pattern was 2.04 (95%CI: 1.44-2.89) and 9.08 (95%CI: 4.54-18.14) times higher, respectively, compared with the high deprivation municipalities. This study identified three patterns of quality of child care, with most teams characterized by an “intermediate adequacy” pattern. The quality of care was associated with the municipality’s characteristics. The methodology used in this study proved effective in characterizing the quality of care in a more consistent way.

Resumo O objetivo foi mapear evidências sobre a qualidade dos registros do Sistema de Informação sobre Mortalidade (SIM) por meio de revisão de escopo nacional com artigos publicados até abril de 2023 em cinco bases (PubMed, Embase, scopus, LILACS e SciELO). Excluíram-se estudos focados em causas específicas de óbito. Foram avaliadas dimensões como acessibilidade, clareza metodologica, cobertura, completitude, confiabilidade, consistencia, nao-duplicidade, oportunidade, validade e estudos de causas mal definidas/garbage codes.. Dos 33 artigos incluídos, observou-se melhoria geral da qualidade do SIM, com redução de 68,5% nos garbage codes (2000-2015). Contudo, persistem desigualdades regionais no SIM, com cobertura abaixo de 90% no Acre e Maranhão e pior desempenho nas regiões Norte e Nordeste. Um SIM robusto é essencial para estratégias de saúde pública voltadas à prevenção de mortes evitáveis.