Assessment of Structural Barriers and Racial Group Disparities of COVID-19 Mortality With Spatial Analysis

COVID-19 mortality among kidney transplant candidates is strongly associated with social determinants of health.

BackgroundData regarding the geographical distribution of cases and risk factors for COVID-19 death in children and adolescents are scarce. We describe the spatial distribution of COVID-19 cases and deaths in paediatric population and their association with social determinants of health in Brazil.MethodsThis is a population-based ecological study with a spatial analysis of all cases and deaths due to COVID-19 in Brazil among children and adolescents aged 0–19 years from March 2020 to October 2021. The units of analysis were the 5570 municipalities. Data on COVID-19 cases and deaths, social vulnerability, health inequities, and health system capacity were obtained from publicly available databases. Municipalities were stratified from low to very high COVID-19 incidence and mortality using K-means clustering procedures, and spatial clusters and relative risks were estimated using spatial statistics with Poisson probability models. The relationship between COVID-19 estimates and social determinants of health was explored by using multivariate Beta regression techniques.FindingsA total of 33,991 COVID-19 cases and 2424 deaths among children and adolescents aged 0–19 years were recorded from March 2020 to October 2021. There was a spatial dependence for the crude mortality coefficient per 100,000 population in the paediatric population aged 0–19 years (I Moran 0·10; P < 0·001). Forty municipalities had higher mortality rates, of which 20 were in states from the Northeast region. Seven spatial clusters were identified for COVID-19 mortality, with four clusters in the Northeast region and three in the North region. Municipalities with higher social inequality and vulnerability had higher COVID-19 mortality in the paediatric population.InterpretationThe main clusters of risk for mortality among children and adolescents were identified in municipalities in the North and Northeast regions, which are the regions with the worst socioeconomic indicators and greatest health disparities in the country. Our findings confirmed the higher burden of COVID-19 for Brazilian paediatric population in municipalities with higher social inequality and vulnerability and worse socioeconomic indicators. To reduce the burden of COVID-19 on children, mass immunisation is necessary.FundingNone.

Social Determinants of Health and Traumatic Brain Injury: Implications for Rehabilitation Service Delivery and Outcomes.

High COVID-19 mortality among Black communities heightened the pandemic's devastation. In the state of Louisiana, the racial disparity associated with COVID-19 mortality was significant; Black Americans accounted for 50% of known COVID-19-related deaths while representing only 32% of the state's population. In this paper, we argue that structural racism resulted in a synergistic framework of cumulatively negative determinants of health that ultimately affected COVID-19 deaths in Louisiana Black communities. We identify the spatial distribution of social, environmental, and economic stressors across Louisiana parishes using hot spot analysis to develop aggregate stressors. Further, we examine the correlation between stressors, cumulative health risks, COVID-19 mortality, and the size of Black populations throughout Louisiana. We hypothesized that parishes with larger Black populations (percentages) would have larger stressor values and higher cumulative health risks as well as increased COVID-19 mortality rates. Our results suggest two categories of parishes. The first group has moderate levels of aggregate stress, high population densities, predominately Black populations, and high COVID-19 mortality. The second group of parishes has high aggregate stress, lower population densities, predominantly Black populations, and initially low COVID-19 mortality that increased over time. Our results suggest that structural racism and inequities led to severe disparities in initial COVID-19 effects among highly populated Black Louisiana communities and that as the virus moved into less densely populated Black communities, similar trends emerged.

Social determinants of racial health inequities

Heart failure (HF) hospitalization readmissions are associated with a high mortality rate and strain the health care system. Both clinical factors and social determinants of health (SDOH) predict HF readmissions, but the optimal approach to incorporating area-level SDOH data remains unclear. We merged census tract- and county-level SDOH measures with electronic health record data in a retrospective cohort of 33 579 Black and White patients with HF (Emory Healthcare, 2010-2018). Six combinations of electronic health record data with 752 area-level SDOH were evaluated using multiple machine learning models (logistic regression, random forest, XGBoost) to predict 30-day HF readmission. Models were assessed for predictive performance using area under the receiver operating characteristic curve and algorithmic fairness across race using the equalized odds ratio. Expanded SDOH predictor sets improved predictive performance and algorithmic fairness compared with traditional SDOH indices. The XGBoost model using expanded SDOH alongside clinical predictors provided better predictive performance (area under the receiver operating characteristic curve, 0.671) and improved algorithmic fairness across patient race (equalized odds ratio, 0.437) than models with traditional indices (area under the receiver operating characteristic curve, 0.632; equalized odds ratio, 0.329). Feature-importance analysis revealed that specific environmental predictors (housing cost burden, air quality) ranked among top predictors alongside clinical biomarkers. County-level SDOH outperformed census tract-level SDOH and matched prediction performance of clinical predictors alone. Including hundreds of individual SDOH indicators rather than traditional composite indices improves machine learning models' ability to predict 30-day HF readmissions. While performance gains are modest, inclusion of specific environmental factors may provide greater clinical utility and improved equity across racial groups than composite SDOH measures for guiding further research and preventive interventions.

e17086 Background: Use of AAB (abiraterone acetate, apalutamide or enzalutamide) has been shown to improve survival in mHSPC, but many patients are not prescribed this treatment. There is substantial interest in the broader role of SDOH in cancer. We explored how SDOH influences prescribing patterns of AAB in patients with mHSPC. Methods: Patients diagnosed with mHSPC between 1/1/2017 and 12/31/2021 were identified using the iKnowMed electronic health record database from The US Oncology Network of community oncology practices. Records were searched for prescriptions for AAB (based on intent to treat). Individual level measures were age, diagnosis year, ECOG, race and type of health insurance. Area level measures were Area Deprivation Index (ADI) at national and state level, and rural status. ADI is a validated metric based on demographic variables from census block groups; high ADI scores for state ( &gt; 8) and national ( &gt; 80) level are markers of low socioeconomic status. Logistic regression models were run on each SDOH variable and adjusted for confounding variables, including a joint distribution model of African American (AA) and ADI (significance at p &lt; 0.05). Results: There were 3,855 patients identified with mHSPC: 40% had a prescription for AAB. Summary measures overall were mean age: 71 years, AA: 10%, Medicaid: 5%, ECOG 0-1 and 2+: 60% and 14% respectively; and diagnosis year 2017-21: 15%, 20%, 21%, 22%, and 21% respectively. Area level scores: National ADI high: 8%, State ADI high: 18%, rural: 5%. Interaction variable: AA + State ADI (high): 2%. Statistical differences between those with/without AAB (not shown) were diagnosis year, ECOG and age. Multivariate logistic models were adjusted for these 3 variables with the single SDOH measure as the primary independent variable and the binary variable AAB as the dependent variable (Table). Conclusions: There was no significant association between AAB prescriptions and AA race, rural status, ADI (state or national), or Medicaid status. These data indicate that SDOH measures do not appear to influence prescribing of these treatments. We believe this study is among the first to examine these particular SDOH measures and their relationship to the prescribing of oncology treatments. Further research should be conducted into the impact of SDOH measures on the fulfillment and compliance of these drugs. [Table: see text]

IntroductionIndividual‐ and neighborhood‐level social determinants of health (SDOH) have been assessed separately in pregnancy, but their relationship to one another remains uncertain. We investigated the intersectionality of three neighborhood‐level SDOH measures with three individual‐level SDOH measures. This was done to examine the concomitant experiences of multiple SDOH in pregnancy.MethodsA secondary analysis of data from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers‐To‐Be. We assessed three neighborhood‐level SDOH measures using geocoded participant home addresses in the first trimester at the census‐tract level: (1) high socioeconomic disadvantage (in tertiles) by the 2015 Area Deprivation Index, (2) inadequate food access by the USDA Food Access Research Atlas, and (3) low walkability by the EPA National Walkability Score. We assessed three individual‐level SDOH measures: low household income, lower educational attainment, and Medicaid insurance. We examined the combinations of these three neighborhood SDOH and three individual SDOH measures by graphical visualization and using statistical tests to assess overall differences in the distribution of these measures.ResultsOf 9588 nulliparous individuals, adverse neighborhood‐level SDOH [high socioeconomic disadvantage (28%), inadequate food access (24%), and low walkability (66%)] and adverse individual‐level SDOH [low household income (19%), lower educational attainment (23%), and Medicaid insurance (33%)] were common in early pregnancy. Six percent of individuals lived in a community with all three adverse neighborhood‐level SDOH measures. Of those living in a community with at least two neighborhood‐level SDOH measures, 23% lived in areas with inadequate food access and low walkability, 19% with high socioeconomic disadvantage and low walkability, and 1% with high socioeconomic disadvantage and inadequate food access. Overall, 23% lived in a community with no adverse neighborhood‐level SDOH, and among this group, 88% had no adverse individual‐level SDOH. There were significant differences in adverse individual‐level SDOH based on whether individuals lived in a community with all three adverse neighborhood‐level measures [low household income (39%), lower educational attainment (44%), Medicaid (55%)], any two measures [low household income (22%), lower educational attainment (27%), Medicaid (37%)], or only one measure [low household income (14%), lower educational attainment (17%), Medicaid (27%)] (p &lt; 0.001 for all).ConclusionAmong nulliparous individuals in early pregnancy, the frequency of adverse individual‐level SDOH was generally higher when they lived in communities with more adverse neighborhood‐level SDOH. Future approaches that identify and classify the multifaceted and multilevel nature of structural determinants as they relate to pregnancy outcomes are needed.

The US Asian American (AA) population is projected to double by 2050, reaching ~43 million, and currently resides primarily in urban areas. Despite this, the geographic distribution of AA subgroup populations in US cities is not well-characterized, and social determinants of health (SDH) and health measures in places with significant AA/AA subgroup populations have not been described. Our research aimed to: 1) map the geographic distribution of AAs and AA subgroups at the city- and neighborhood- (census tract) level in 500 large US cities (population ≥66,000); 2) characterize SDH and health outcomes in places with significant AA or AA subgroup populations; and 3) compare SDH and health outcomes in places with significant AA or AA subgroup populations to SDH and health outcomes in places with significant non-Hispanic White (NHW) populations. Maps were generated using 2019 Census 5-year estimates. SDH and health outcome data were obtained from the City Health Dashboard, a free online data platform providing more than 35 measures of health and health drivers at the city and neighborhood level. T-tests compared SDH (unemployment, high-school completion, childhood poverty, income inequality, racial/ethnic segregation, racial/ethnic diversity, percent uninsured) and health outcomes (obesity, frequent mental distress, cardiovascular disease mortality, life expectancy) in cities/neighborhoods with significant AA/AA subgroup populations to SDH and health outcomes in cities/neighborhoods with significant NHW populations (significant was defined as top population proportion quintile). We analyzed AA subgroups including Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, and Other AA. The count and proportion of AA/AA subgroup populations varied substantially across and within cities. When comparing cities with significant AA/AA subgroup populations vs NHW populations, there were few meaningful differences in SDH and health outcomes. However, when comparing neighborhoods within cities, areas with significant AA/AA subgroup vs NHW populations had less favorable SDH and health outcomes. When comparing places with significant AA vs NHW populations, city-level data obscured substantial variation in neighborhood-level SDH and health outcome measures. Our findings emphasize the dual importance of granular spatial and AA subgroup data in assessing the influence of SDH in AA populations.

Dear Editor Previous research found that childhood cancer survivors of African ancestry have significantly higher morbidity and mortality than those of European ancestry [1]. However, after adjusting for socio-economic factors, the magnitudes of racial health disparities are either substantially decreased or become statistically non-significant [1], suggesting that social and economic determinants may contribute to racial health disparities. Recently, we conducted epigenome-wide association studies (EWAS) for three key social determinants of health (SDOHs), namely, personal educational attainment, personal income, and neighborhood deprivation among survivors of childhood cancer, where 130 epigenome-wide significant SDOH-CpG associations were identified among European ancestry survivors, and 25 of which were also validated in African ancestry survivors [2]. Notably, many SDOH-associated CpG sites are also associated with tobacco use. Although pulmonary impairment is an integral part of the overall disease burden, racial disparities in this specific group of conditions have not been documented, and potential underlying mechanistic causal pathways have not been studied. Moreover, other observational studies have shown that blood DNA methylation (DNAm) signature was associated with pulmonary functions [3-5]. In this cross-sectional study, we hypothesized that race and its associated SDOHs might contribute to the risk of pulmonary impairment, evaluated whether SDOH-associated CpG sites were associated with specific parameters of pulmonary function, and further applied mediation analysis to explore the potential mediating role of these DNAm sites for the association between SDOHs and risk of impaired pulmonary functions. The methods are described in Supplementary Methods and Supplementary Table S1. The occurrence rates of three adverse pulmonary outcomes, obstructive pulmonary deficit (OPD), pulmonary diffusion deficits (PDD), and restrictive pulmonary deficit (RPD), were compared between African- and European ancestry survivors in the St. Jude Lifetime Cohort (SJLIFE) study [6]. The study population is described in the Supplementary Results and Supplementary Table S2. In an unadjusted model considering common terminology criteria for adverse event (CTCAE) grade ≥2, the occurrence rates of PDD and RPD were significantly higher in African ancestry survivors than in European ancestry survivors (PDD: 25.2% vs. 18.2%, P = 0.033; RPD: 14.2% vs. 7.5%, P = 0.002), whereas OPD was comparable between the two race groups (9.8% vs. 13.1%, P = 0.206) (Supplementary Figure S1). In a multivariable model, adjusting for other covariates but without the inclusion of SDOHs (the base model), race was significantly associated with PDD (P = 0.024) and RPD (P = 0.004, Supplementary Table S3). When SDOHs were added to the model (the full model), the effect of race on pulmonary impairment became non-significant for PDD (P = 0.183), slightly attenuated for RPD (P = 0.006) and remained non-significant for OPD (P = 0.434). Notably, treatment factors, including chest RT and lung surgery, were significantly associated with all three conditions. The effect of current smokers became non-significant for OPD and PDD. Interestingly, BMI was inversely associated with PDD. Because SDOHs only accounted for the racial disparity in PDD from the above analyses, we further analyzed if SDOH-associated CpG sites could mediate the association between SDOHs and PDD. Among the 130 SDOH-CpG associations identified in our previous EWAS on European ancestry survivors [2], 61 CpGs (29 for educational attainment, 16 for personal income, and 16 for area deprivation index [ADI]) were significantly associated with the risk of PDD after adjusting for multiple comparisons (Pfalse discovery rate (FDR) < 0.050) (Supplementary Table S4), some of which have been previously reported to be associated with health conditions related to pulmonary functions. In the mediation analysis, 17 out of 29 educational attainment-associated CpGs were identified with significant average causal mediation effects (ACME) after adjusting for multiple comparisons. Using a squared pairwise Pearson correlation coefficient r2 threshold of 0.05, three independent CpGs, cg04180924 (chr3, coproporphyrinogen oxidase [CPOX], mediation = 32.9%, PFDR = 0.014), cg11205006 (chr22, HPS4, mediation = 19.0%, PFDR = 0.024), and cg27470486 (chr17, ATP citrate lyase [ACLY], mediation = 8.6%, PFDR = 0.044), were obtained by top-down pruning the 17 CpGs sorted by estimated ACME in decreasing order. For the final mediation analysis, a combined score (i.e., summation of DNAm levels of the three CpGs with the same direction of association) was used as the mediating variable, and a 48.9% mediation effect for educational attainment on PDD was achieved (Table 1). Similarly, the same single mediator, cg08064403, partially mediated the effect of personal income (mediation = 25.9%, P < 0.001) and ADI (mediation = 24.1%, P < 0.001) on PDD (Table 1). None of the SDOH-associated CpG sites was significantly associated with the risk of PDD among African ancestry survivors after adjusting for multiple comparisons because of the small number of African-ancestry survivors, hence similar mediation analysis could not be conducted among African ancestry survivors. For each CpG mediating the association between SDOHs and pulmonary conditions in European ancestry survivors, a linear regression of expression levels for Illumina-annotated genes against DNAm levels of CpGs was performed. The DNAm levels of four CpGs were negatively correlated with the gene expression levels of Illumina-annotated genes: ACLY (cg27470486), which plays a role in lipid synthesis in the lung [7]; Hermansky-Pudlak syndrome 4 (HPS4) (cg11205006), which is related to pulmonary fibrosis [8]; CPOX (cg04180924 and cg08064403) [9] and claudin domain containing 1 (CLDND1) (cg08064403) [10], two smoking-related genes (Supplementary Figures S2-S3 and Supplementary Table S5). Based on these correlations between DNAm and gene expression levels, all four CpGs were deemed as expression quantitative trait methylations. We leveraged the molecular profiling data of the well-established SJLIFE cohort and provided strong evidence supporting social-epigenetic mediators for racial disparities in pulmonary impairment among childhood cancer survivors. The present study had some limitations. First, the analysis was based on a relatively short follow-up from blood drawn for DNAm detection, so there was no clearly defined temporal association to establish the causality. Second, we attempted to take advantage of the existing whole-genome sequencing data to search for methylation quantitative trait loci (meQTL), but we did not find any strong meQTL for the genomic regions of interest that could be used in Mendelian randomization for causal inference. Third, air pollution from neighborhoods or occupational exposures and second-hand smoke, which may also contribute to pulmonary impairment, were also not considered due to the lack of data. Lastly, our analysis was based on each individual condition separately, other factors including co-morbidity (e.g., among 209 survivors with PDD, 70 had OPD, 53 had RPD, and 42 had both OPD and RPD), type and stage of primary diagnosis may also confound the results. In conclusion, the risk of pulmonary impairment among survivors of childhood cancer differs by specific condition (PDD or RPD) and race (African and Europena ancestry). SDOHs may partially explain the observed racial disparity in PDD, potentially through an epigenetic mechanism. Social-epigenetic studies like ours could inform intervention strategies, such as improving social integration and social support to counteract the elevated disease risk for social-economically disadvantaged survivors. The efficacy of this type of intervention can be objectively measured by the improvement of epigenetic markers as an intermediate outcome. Ultimately, we will close the gap of disparity in pulmonary impairment and other health outcomes due to race or social adversity among survivors of childhood cancer. ZW and ICH designed and supervised the study; MMH, KKN, KRK and LLR assisted in or provided support for data collection and recruitment of study participants; JE, HM, EP, GN, EW, and JZ supervised sample processing, and/or performed DNA/RNA extractions, carried out the Infinium MethylationEPIC array scanning and RNA sequencing; NS, QD, CC, QL, DKS, ICH, and ZW performed bioinformatic and statistical analysis; NS, QD, ICH, and ZW wrote the first draft of the manuscript. All authors contributed to data interpretation and writing and approved the final manuscript for publication. Not applicable. The authors declare that they have no competing interests. This work was supported by funding from the V Foundation (Grant # DT2020-014), the National Institutes of Health of the US (Grant # CA021765, CA195547) and the American Lebanese Syrian Associated Charities (ALSAC). The funders of the study had no role in the design and conduct of the study; were not involved in collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication. DNA methylation data are accessible at NCBI Gene Expression Omnibus database under the accession number GSE169156 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE169156). Additional clinical data about the study participants in the St. Jude Lifetime Cohort can be accessed via the survivorship portal (http://survivorship.stjude.cloud/). The SJLIFE study protocol was approved by the Institutional Review Board (IRB) at St. Jude Children's Research Hospital with a reference number (010882). All SJLIFE study participants provided written informed consent. This study was performed in accordance with the Declaration of Helsinki. Not applicable. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Structural Justice Ethics in Health Care

Understanding Social Determinants of Brain Health During Development.

Background: According to the Georgia Department of Public Health (GDPH), the state of Georgia reported 563,658 cumulative COVID-19 cases and 9,845 total deaths in 2020. Decades of research on racial disparities in health outcomes suggest we should not be surprised with the disproportionate number of cases, hospital visits, and deaths of non-white and Black Georgia residents. Racial disparities in health are often defined by a Social Determinants of Health (SDOH) model. One understudied SDOH is racial residential segregation. In this study we explore the relationship between racial residential segregation and COVID-19 health outcomes. Our paper addresses critical challenges for racial health disparities research with guidance for legal and policy approaches to the reduction of racial health disparities. Methods: The 2020 University of Wisconsin Population Health Institute and GDPH datasets were used to explore the relationship between segregation and COVID-19 health outcomes in Georgia. Independent variables included those SDOH most associated with racial health disparities. Dependent variables were COVID-19 case rate, hospitalization rate and death rate. Results: Our findings suggest that racial residential segregation is directly associated with Black COVID-19 case rate and indirectly associated with Black hospitalization and death rate through its effect on Black case rate. Conclusions: Racial residential segregation is an often-overlooked SDOH since it is secondary to more prominent social issues such as education and economic opportunities for reducing racial disparities in health. For the Black population, especially for public health issues like COVID-19, we make the case for a better focus on racial residential segregation policy, as a social and economic factor for disease transmission. The ultimate goal is to improve health outcomes for all. A focus on racial residential segregation policy can impact better prepared health services entities and ultimately improve population health. Key words: Racial disparities in health, Racial residential segregation, COVID-19, Social Determinants of Health

The sudden and rapid advancement of the novel Coronavirus (COVID-19) pandemic has led to an unanticipated and unprecedented global crisis Since its emergence in the United States, there is increasing discussion surrounding the impact of the virus among vulnerable populations Older adults, young children, and persons with chronic medical or mental health conditions, persons with disabilities, pregnant women, immunocompromised persons and those who are institutionalized or homeless are considered most vulnerable to death and lost quality of life (World Health Organization, 2020)

BackgroundDisparities in non-medical health factors, such as social determinants of health (SDoH), are associated with increased risk of negative health outcomes. Leveraging contextual (or area-based) measures of SDoH is essential for uncovering broader factors influencing disparities in critical care-related outcomes. Our objective is to review evidence analyzing the association between contextual SDoH obtained from publicly available databases and critical care-related outcomes in the United States (US).MethodsWe conducted searches in the Web of Science, PubMed, Cochrane, and Embase electronic database to obtain clinical studies utilizing SDoH datasets from publicly available data sources and analyzed these studies for associations between critical care-related outcomes and SDoH (search date June 8th, 2025). We excluded non-English articles, reviews, editorial commentaries, letters to editors, studies without intensive care unit (ICU) patients or SDoH variables, studies based on countries outside of the US and studies that lacked full text or contained only the abstract. We extracted cohort characteristics, SDoH measures and domains, SDOH database characteristics, ICU admissions and outcomes, analytical method used for determining the association between SDoH and ICU variables, and significant SDoH variables.ResultsWe identified 87 publications (44 with pediatric patients, 40 with adult patients, and 3 with a mixture of both) and study population characteristics (e.g., surgical or specific disease-diagnosed patients). Child Opportunity Index and American Community Survey were the top platforms utilized for acquiring SDoH in pediatric and adult cohorts, respectively, followed by Area Deprivation Index and Social Vulnerability Index in both cohort types. Area-level granularity included boundaries determined by counties, ZIP codes, census block groups and census tracts.ConclusionsAmong five SDoH domains, economic stability was found to be the top investigated SDoH category for critical care-related outcomes. Contextual SDoH variables, indicating more vulnerable and adverse conditions, were associated with higher ICU admissions, greater need for ICU resource utilization, longer ICU duration, higher likelihood of developing critical illnesses, worsened life quality following ICU discharge, and higher mortality. Social determinants of health offer a broad area for modifiable intervention targets. Public databases serve as facilitators towards SDoH integration into electronic health records, promoting value-based care and mitigating health inequities.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13054-025-05622-1.