ABSTRACT Massification has transformed higher education from elite to high-participation and, in some contexts, universal systems. This expansion has intensified horizontal differentiation among institutions and vertical stratification of students, resources, and status. This comparative study examines how these forces shape student affairs and services (SAS) across nine systems – Mauritius, Singapore, Cambodia, India, Bangladesh, China, Brazil, South Africa, and South Korea. Drawing on institutional theory, we identify five institutional logics – access, equity, quality, market, and global – local adaptation – and show how they are operationalised and contested across governance, funding, functions, target populations, and professionalisation. Incorporating contemporary political pressures, challenges to academic freedom, and shifts in student mobility, we advance an explanatory typology linking constellations of logics to distinct SAS configurations. Findings reveal how hybrid arrangements can either mitigate or reproduce inequalities. We conclude with context-sensitive policy strategies to rebalance equity and market imperatives while adapting international models to local conditions.

An existential exploration of Dalit subjectivity in Bama's short fiction “Annachi” and “Chilli Powder”

Abstract: Concerned by competitive grantmaking’s role in the racialized (re) production of inequality, we applied a Bourdieusan lens and postsecondary racial neoliberalism to examine Title V—a competitive, federal grant program for Hispanic-serving institutions (HSIs) poised to mitigate racially patterned funding inequality. Specifically, employing a critical qualitative methodological approach, we interviewed 29 institutional actors across 17 HSIs and showed that even an equity-aspiring program like Title V could (re)produce inequality among HSIs and magnify the racialized stratification of resources across the field, particularly given the institutional and resource diversity among HSIs. Precisely, seemingly at odds with Title V’s goals and the possibilities of Hispanic-servingness, this neoliberal program is set to privilege places with better grant-related infrastructure, more money, and other less tangible resources (e.g., grantisan knowledge). To better realize this federal investment’s transformative potential, we emphasize implications for public policy and offer possibilities for HSI scholars and practitioners, particularly in grant development and administration.

BackgroundThe poor prognosis of hypertensive cerebral hemorrhage (HICH) remains high. The period of 3–6 months after onset is the most rapid phase of neurological recovery in hemorrhagic stroke patients. Accurate early prediction of 6-month functional outcomes is critical for optimizing therapeutic strategies. This study compared the predictive efficacy of multiple machine learning models to identify the optimal model for forecasting long-term prognosis in HICH patients.MethodsWe conducted a retrospective analysis of clinical data from 807 HICH patients admitted to Qinghai Provincial People's Hospital's Neurosurgery Department between June 2020 and June 2024. After data preprocessing, data from June 2020 to December 2023 (n = 716) were randomly split into training (n = 497) and test sets (n = 219) at a 7:3 ratio. Data from January to June 2024 (n = 91) served as an external validation set. Recursive Feature Elimination (RFE) was performed to identify optimal features, and repeated five-fold cross-validation minimized the risk of overfitting. Model performance was evaluated using Area Under the Curve (AUC) and Decision Curve Analysis (DCA) across XGBoost, Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). The optimal model was interpreted via SHapley Additive exPlanations (SHAP).ResultsThe 6-month poor prognosis rate among 807 HICH patients was 27.51%. The XGBoost model exhibited optimal performance in the training set (AUC = 0.921, 95% CI: 0.896–0.944) and demonstrated stability in the external validation set (AUC = 0.813, 95% CI: 0.728–0.899). DCA analysis showed that the XGBoost model provided higher net benefit than other models across threshold probabilities of 0%−20% and 56%−100%. SHAP analysis identified hematoma volume as the most critical predictor, with secondary contributions from Glasgow coma score, white blood cell count, age, serum albumin, and systolic blood pressure, among others.ConclusionXGBoost models demonstrate powerful accuracy in long-term prognosis prediction of HICH patients. The SHAP framework quantifies the specific contributions of key pathophysiological indicators to individual patient model predictions, enabling individualized risk stratification and strategic allocation of medical resources.

The search for extraterrestrial life hinges on identifying biosignatures, often focusing on gaseous metabolic byproducts as indicators. However, most such biosignatures require assuming specific metabolic processes. It is widely recognized that life on other planets may not resemble that of Earth, but identifying biosignatures “agnostic” to such assumptions has remained a challenge. Here, we propose a novel approach by considering the generic outcome of life: the formation of competing ecosystems. We use a minimal model to argue that the presence of ecosystem-level dynamics, characterized by ecological interactions and resource competition, may yield biosignatures independent of specific metabolic activities. Specifically, we propose the emergent stratification of chemical resources in order of decreasing energy content as a candidate new biosignature. While likely inaccessible to remote sensing, this signature could be relevant for sample return missions, or for detection of ancient signatures of life on Earth itself.

On the basis of the qualitative research conducted on the basis of several regional and central Georgian universities, the article presents the development prospects of media schools and alternative models for Georgian regional media schools. Results of the research showed that regional university education in the direction of digital media in Georgia is in a critical state, suffering from forms of inequality, both in terms of educational bases and human professional resources. As a result, young people choose central universities, which creates unequal opportunities for the development of the regional parts of Georgia. The situation of regional universities is becoming even more problematic; in the research, we tried to show that this process will bring disastrous results for the whole country if there is no professional distribution, redistribution of media students across the country and purposeful stratification of intellectual resources.

Concerned by competitive grantmaking’s role in the racialized (re) production of inequality, we applied a Bourdieusan lens and postsecondary racial neoliberalism to examine Title V—a competitive, federal grant program for Hispanic-serving institutions (HSIs) poised to mitigate racially patterned funding inequality. Specifically, employing a critical qualitative methodological approach, we interviewed 29 institutional actors across 17 HSIs and showed that even an equity-aspiring program like Title V could (re)produce inequality among HSIs and magnify the racialized stratification of resources across the field, particularly given the institutional and resource diversity among HSIs. Precisely, seemingly at odds with Title V’s goals and the possibilities of Hispanic-servingness, this neoliberal program is set to privilege places with better grant-related infrastructure, more money, and other less tangible resources (e.g., grantisan knowledge). To better realize this federal investment’s transformative potential, we emphasize implications for public policy and offer possibilities for HSI scholars and practitioners, particularly in grant development and administration.

BackgroundWith the accelerating global population aging, the proportion of elderly patients with colorectal cancer (CRC) undergoing laparoscopic radical resection is increasing annually. However, significant individual variations in postoperative hospital stay exist, and convenient clinical prediction tools remain lacking. This study aimed to develop and validate a simplified predictive scoring model for postoperative hospital stay in elderly CRC patients after laparoscopic radical resection.Materials and methodsA total of 205 elderly CRC patients (≥70 years) who underwent laparoscopic radical resection at our hospital from August 2024 to September 2025 were retrospectively included. Baseline characteristics (age, sex, BMI, comorbidities), tumor indicators (location, TNM stage), surgical parameters (operative time, blood loss, stoma creation), preoperative and postoperative blood markers (albumin, hemoglobin, direct bilirubin), perioperative assessments (ASA classification), and postoperative outcomes (30-day complications, hospital stay) were collected. Postoperative hospital stay (excluding delays due to non-medical factors) served as the primary outcome. Univariate linear regression identified potential influencing factors, and multivariate linear regression determined independent risk factors. A predictive scoring model was constructed based on independent factors, with efficacy validated using the coefficient of determination (R2) and root mean square error (RMSE). Risk stratification was performed to analyze differences in hospital stay across scoring tiers.ResultsThe mean postoperative hospital stay was 16.6 ± 5.0 days (range: 9–42 days). multivariate linear regression analysis revealed that 30-day postoperative complications were an independent risk factor for prolonged hospital stay (β = 7.689, P < 0.001). A simplified scoring model was developed: postoperative complications (present = 3 points, absent = 0 points), ≥2 comorbidities (yes = 1 point, no = 0 points), and operative time >180 min (yes = 1 point, no = 0 points), yielding a total score range of 0–5 points. Risk stratification showed: low-risk group (0 points) had an expected stay of 14.8 ± 3.2 days (15% of patients), medium-risk group (1–2 points) 16.0 ± 4.0 days (60%), and high-risk group (3–5 points) 22.5 ± 6.8 days (25%).ConclusionThe simplified scoring model developed in this study effectively predicts postoperative hospital stay in elderly CRC patients undergoing laparoscopic radical resection, providing a practical tool for clinical risk stratification, early intervention, and optimization of healthcare resources.

Background; Covid-19 pneumonia was a pandemic which lead to relentless acute respiratory failure caused by SARS-CoV2 virus. The cytokine storm is responsible for hypoxia and multiorgan failure. Various serum markers have been identified as key factors in the progression to COVID-19 pneumonia. Although pandemic is over after worldwide vaccination by messenger RNA vaccine yet there are still cases being reported to death in few countries of world. Therefore, it is important to further elaborate the key factor like elevated serum urea to albumin ratio for risk stratification and allocation of resources to avoid mortality in COVID-19 pneumonia. Objective: To evaluate the association between the serum urea-to-albumin ratio and the mortality risk in the early days of ICU admission among patients with COVID-19 pneumonia. Methods: This retrospective study was conducted on eighty-two COVID-19 patients hospitalized in the ICU. These patients were admitted to the ICU for at least four days with COVID-19 pneumonia between March 1 and July 30, 2021, at Lahore Health Care Hospital. Diagnosis was confirmed by RT-PCR on nasal swab along with characteristic opacities in both lower lung zones on radiological imaging. Patients were on high-flow oxygen via rebreathing mask, non-invasive positive pressure ventilator support, or mechanical ventilator. Serum albumin levels and urea were measured daily from the first day to the fourth day of ICU admission. Results: Out of 82 patients of COVID-19 pneumonia who remained admitted in the ICU for four days, 38 patients expired. The mean age was 59.1 ± 13.1 years, and 62% (n = 52) were male. Nearly half of the patients (n = 42) were aged 60 years or older. The mean U/A ratio for discharged patients was 13.60 on day 1 and 12.30 on day 4, while for expired patients it was 21.09 on day 1 and 35.06 on day 4. An increased U/A ratio was significantly associated with a higher risk of death (p &lt; 0.001) on both day 1 and day 4, with a stronger correlation observed on day 4 (r = 0.55) compared to day 1 (r = 0.29). Increased age was not correlated with mortality, and no significant association was found between gender and mortality. Conclusion: Elevated serum U/A ratio at admission is a strong predictor of disease severity and mortality in COVID-19 pneumonia. Its routine assessment may aid in early risk stratification and clinical decision-making.

Troubled by the inequities in competitive grantmaking, we use critical quantitative methods to analyze the FY2023 federal academic earmarks as a potential mechanism for racialized change work. Specifically, we ask: To what extent does Congress distribute academic earmarks in ways that reinforce or weaken the racialized stratification of resources across organizations in the field? Accordingly, we identify distribution patterns of academic earmarks, considering the allocation of dollars and types of earmarks (i.e., general capacity-building versus specialized grants) across colleges and universities, between White-serving institutions and minority-serving institutions (MSIs), and among MSIs. Based on our analysis, Congress favored a racially reproductive funding portfolio, driven by smaller and more restrictive allocations, not fewer earmarks. However, the distribution of earmarks among MSIs defied normed expectations, as Congress did not privilege whiter, more prestigious MSIs, signaling the potential of pork-barrel politics for racially reparative work.

Frailty is associated with adverse health outcomes and can be measured using the FRAIL scale. In Singapore, its use has been studied in tertiary hospitals but not in community hospitals. A tool to predict rehabilitation outcomes would allow for better risk stratification and allocation of resources. We aimed to determine whether the FRAIL scale is associated with rehabilitation outcomes in patients admitted to the community hospital setting, where post-acute care and rehabilitation are primarily delivered. This was a retrospective cohort study. The FRAIL scale was utilised to screen 560 older adults who were admitted to a community hospital for rehabilitation. Data were analysed to determine the relationship between baseline characteristics and frailty status, with rehabilitation outcome measures of absolute functional gain, rehabilitation effectiveness, rehabilitation efficiency, length of stay and discharge destination. The combined score of the FRAIL scale showed significant negative association with absolute functional gain (P < 0.001), rehabilitation effectiveness (P < 0.001) and rehabilitation efficiency (P < 0.001), whereas it was positively associated with increased length of stay (P < 0.05) and a need for continued support in increased care settings (P < 0.001). Individual components of the FRAIL scale, in particular, the 'fatigue', 'ambulation' and 'loss of weight' components, appeared to be highly associated with rehabilitation effectiveness and efficiency, especially among pre-frail patients. The utility of the FRAIL scale as an indicator of frailty status and its association with rehabilitative outcomes in the post-acute care setting were demonstrated. Moreover, the FRAIL scale may better predict the rehabilitative progress of pre-frail patients.

In light of COVID-19 infection control measures, which establish a minimum distance of 6 feet between bodies, many have emphasised the need to maintain social closeness despite physical distance. This Position Piece considers the flip side of this concern: in key spaces that structure social interactions in the US today, physical closeness does not equal social closeness. ‘This country is like a prison,’ one of my interlocutors told me, pointing to carceral histories of social distancing that predateCOVID-19. Moreover, in the co-constituted spaces of criminalisation and justice, punishment and care, distance and proximity, and carceral freedom, the physical closeness of people that couldhave registered as social closeness is precluded, culminating in deadly disregard. Drawing on my medical training and on fieldwork documentation of medical harms in the US Immigration and Customs Enforcement (ICE) detention apparatus, I consider how non-responsiveness spreads within and beyond institutions of immigration enforcement. I suggest that medical providers are implicated in what I call ‘contagious containment’—that is, the impulse to distance oneself from harmful realities in which one’s clinical practice is complicit. This distancing reinforces the idea of humanity being a scarce resource, especially when the racialised stratification of economic and political resources is preserved in and through institutions like ICE detention. Contagious containment offers the fantasy of separating one’s clinical work from the apparatus of harming and, so long as such reservoirs of life-threatening disregard remain, such contagion can (and does) spread.

Background and ObjectivesCancer services are under increasing pressure to deliver waiting time targets. Our service has seen referral numbers increase to over 3000 per annum, with more than 80% coming from secondary care. In order to deliver a responsive service, the department has introduced a daily diagnostic multidisciplinary meeting (DMDT) with the aim being stratification of resources by directing rapid access to clinics and diagnostics to those felt to be at greatest risk of malignancy at the start of the pathway. It also aimed to improve communication with patients and referrers, consistency in decision making and deliver improved diagnostic turn-around times in a sustainable manner. An evaluation was undertaken to assess whether the introduction of the DMDT has improved the pathway, the primary endpoint being a reduction in time to definitive diagnosis (TTDD). Secondary endpoints included measurements of efficiency and whether there has been a reduction in variation in practice.MethodsRetrospective access to a prospective database over a 1-month period before (2015) and after (2018) the intervention.ResultsThe introduction of the DMDT has led to a reduction in TTDD (7 days). The service also has an added benefit in reducing average total patient miles travelled over the course of diagnosis by 22.68 miles.ConclusionThe introduction of a diagnostic MDT at the start of the pathway does lead to an improvement in service efficiency and a reduction in TTDD.

After the collapse of the bipolar world, the neoliberal mainstream emerged in the global hierarchy of social sciences, built on three axiomatic pillars: Western domination, capitalism (free market) and liberalism (the value of individual autonomy). Nowadays, one can more and more often witness criticism and disintegration of the mainstream, which claimed the universality of descriptions and legitimation of modern societies that have reached the end of history in the form of open-access liberal market democracies. The purpose of the article is to find out how transformations of the prevailing political and economic orders, configurations of the subjects of geopolitical dominance and their legitimate metaphors determine the direction of changes in the social sciences mainstream. The hypothesis of the research is that the prevailing principles of stratification and distribution of public resources will be less and less valuable and institutionally related to capitalism, market and democracy, since an appeal to the latter does not lead to an increase in the available opportunities for the majority of the population in practice. The global change in social ontology, the structure of economic reproduction and legitimate foundations of the political order bring about a drop in the credibility and relevance of mainstream concepts focused on the axiomatics of market values and liberal rhetoric. Intellectual attempts to restore the relevance of the neoliberal mainstream through the construction of local utopias (flat world, creative class, knowledge economy, etc.), the introduction of complementary concepts of civil repair (J. Alexander), sociocultural trauma (P. Shtompka), unworthy government (bad governance), dependence on previous development (path dependence) or gauge (N. Rozov) do not save from growing conceptual stretch. Formation of a society without tangible economic growth and a declining need for mass labor leads to the inevitable transformation of the mainstream. Alternative and peripheral theories that describe the contours of a global future mainly in non-market, non-capitalist and, possibly, non-liberal categories are becoming more influential. These are concepts that fix new formats for the distribution of public resources, less and less connected with the market, democracy and hegemony of the West, but increasingly – with rental mechanisms, distributive political regulation and differentiated value of different social groups for the national state. Acknowledgements. The article has been supported by a grant of the Russian Foundation for Basic Research (RFBR) and the Expert Institute of Social Research (“опн”). Project no. 20-011-31025 “Alternatives to Political Mainstream amidst the Crisis of the Global World Order”.

BackgroundRisk scores can be useful in clinical risk stratification and accurate allocations of medical resources, helping health providers improve patient care. Point-based scores are more understandable and explainable than other complex models and are now widely used in clinical decision making. However, the development of the risk scoring model is nontrivial and has not yet been systematically presented, with few studies investigating methods of clinical score generation using electronic health records.ObjectiveThis study aims to propose AutoScore, a machine learning–based automatic clinical score generator consisting of 6 modules for developing interpretable point-based scores. Future users can employ the AutoScore framework to create clinical scores effortlessly in various clinical applications.MethodsWe proposed the AutoScore framework comprising 6 modules that included variable ranking, variable transformation, score derivation, model selection, score fine-tuning, and model evaluation. To demonstrate the performance of AutoScore, we used data from the Beth Israel Deaconess Medical Center to build a scoring model for mortality prediction and then compared the data with other baseline models using the receiver operating characteristic analysis. A software package in R 3.5.3 (R Foundation) was also developed to demonstrate the implementation of AutoScore.ResultsImplemented on the data set with 44,918 individual admission episodes of intensive care, the AutoScore-created scoring models performed comparably well as other standard methods (ie, logistic regression, stepwise regression, least absolute shrinkage and selection operator, and random forest) in terms of predictive accuracy and model calibration but required fewer predictors and presented high interpretability and accessibility. The nine-variable, AutoScore-created, point-based scoring model achieved an area under the curve (AUC) of 0.780 (95% CI 0.764-0.798), whereas the model of logistic regression with 24 variables had an AUC of 0.778 (95% CI 0.760-0.795). Moreover, the AutoScore framework also drives the clinical research continuum and automation with its integration of all necessary modules.ConclusionsWe developed an easy-to-use, machine learning–based automatic clinical score generator, AutoScore; systematically presented its structure; and demonstrated its superiority (predictive performance and interpretability) over other conventional methods using a benchmark database. AutoScore will emerge as a potential scoring tool in various medical applications.

Coronavirus Disease 2019 is a very fast-spreading infectious disease. Severe forms are marked by a high mortality rate. The objective of this study is to identify routine biomarkers that can serve as early predictors of the disease progression. This is a prospective, single-center, cohort study involving 330 SARS-CoV-2 infected patients who were admitted at the University Hospital of Blida, Algeria in the period between the 27th of March and 22nd of April 2020. The ROC curve was used to evaluate the predictive performance of biomarkers, assessed at admission, in the early warning of progression toward severity. Multivariate logistic regression was used to quantify the independent risk for each marker. After an average follow-up period of 13.9 ± 3.5 days, 143 patients (43.3%) were classified as severe cases. Six biological abnormalities were identified as potential risk markers independently related to the severity: elevated urea nitrogen (>8.0 mmol/L, OR = 9.3 [2.7–31.7], p < .00001), elevated CRP (>42mg/L, OR = 7.5 [2.4–23.3], p = .001), decreased natremia (<133. 6 mmol/L, OR = 6.0 [2.0–17.4], p = .001), decreased albumin (<33.5 g/L, OR = 5.2 [1.7–16.6], p = .003), elevated LDH (>367 IU/L, OR = 4.9 [1.7–14.2], p = .003) and elevated neutrophil to lymphocyte ratio (>7.99, OR = 4.2, [1.4–12.2], p = .009). These easy-to-measure, time-saving and very low-cost parameters have been shown to be effective in the early prediction of the COVID-19 severity. Their use at the early admission stage can improve the risk stratification and management of medical care resources in order to reduce the mortality rate.

BackgroundFrailty is a widely used measure in older patients as a predictor of poor outcomes after hospitalization and surgery. There is a growing body of data in kidney transplantation suggesting frailty can predict adverse outcomes. There is interest in using chart review measures of frailty and multimorbidity, as they may be equally predictive as physical measurement. This approach holds promise for patient evaluation, identifying candidates for prehabilitation, and targeting resources towards those anticipated to have an increased rate of clinical challenges after kidney transplantation. Frail patients who are often older may place a large resource and economic burden on transplant programs.MethodsWe applied a previously published chart review–based approach in a retrospective, pilot study to calculate the Frailty Risk Score (FRS) utilizing a cohort of kidney transplant patients. We reviewed concurrent comorbidities using the Charlson comorbidity (CM) score to determine the feasibility and utility of applying this approach in transplant patients to predict post-transplant outcomes such as length of hospitalization and the need for rehospitalization.ResultsSixty kidney transplant recipients were evaluated by chart review, 23 characterized as older (> = 60) and 37 younger (ages 30–59). Median FRS score was 3 (range 1–7). Higher FRS was significantly associated with increased patient age (high FRS 19% in younger patients, 43% in older patients). Increased CM score was also associated with increased patient age. Patients with a high FRS stayed in the hospital for an average of 8 days, compared with 5.7 days for a low FRS. Patients with high FRS were readmitted an average of 2.9 times compared with an average of 1.1 for those with a low FRS. FRS score remained significant for predicting outcomes after adjustment for patient age.ConclusionElevated FRS prior to transplantation was associated with increased hospital stay and the need for readmission in kidney transplant recipients. This analysis demonstrates the potential strength of chart review in evaluating frailty prior to transplantation, permitting risk stratification and targeting of resources for rehabilitation and close post-transplant monitoring. Frail patients may benefit from targeted “prehabilitation” to attenuate the associated adverse clinical outcomes.

Microsatellite instability (MSI) is a distinct molecular subtype of gastric cancer. In recent years, the clinical consequences of MSI and the therapeutic opportunities to target this peculiar cancer subtype became evident. However, despite the importance of MSI for the stratification of patients, the time and resources required for diagnosis still present an obstacle. In an attempt to identify a new marker for MSI in gastric cancer, we evaluated the expression of five cancer-associated glycan epitopes in a cohort of 13 MSI and 17 microsatellite stable (MSS) cases. Our analysis revealed a highly significant (p < 0.001) association between the expression of the Thomsen-Friedenreich (TF) antigen and MSI status. Hence, we present here the identification of the first single marker for MSI in gastric cancer, excelling with a specificity of 94% (16/17), sensitivity of 69.2% (9/13), negative predictive value of 80% (16/20), and positive predictive value of 90% (9/10). The TF antigen, detected by simple antibody-based assays, is highly specific for carcinoma being undetectable in gastric healthy and premalignant epithelia. This finding lays the basis for new studies and holds promise in improving the rapid identification of MSI in the clinical setting.

Ascertaining which patients are at highest risk of poor postoperative outcomes could improve care and enhance safety. This study aimed to construct and validate a propensity index for 30-day postoperative mortality. A retrospective cohort study was conducted at Hospital de Clínicas de Porto Alegre, Brazil, over a period of 3 years. A dataset of 13524 patients was used to develop the model and another dataset of 7254 was used to validate it. The primary outcome was 30-day in-hospital mortality. Overall mortality in the development dataset was 2.31% [n = 311; 95% confidence interval: 2.06–2.56%]. Four variables were significantly associated with outcome: age, ASA class, nature of surgery (urgent/emergency vs elective), and surgical severity (major/intermediate/minor). The index with this set of variables to predict mortality in the validation sample (n = 7253) gave an AUROC = 0.9137, 85.2% sensitivity, and 81.7% specificity. This sensitivity cut-off yielded four classes of death probability: class I, <2%; class II, 2–5%; class III, 5–10%; class IV, >10%. Model application showed that, amongst patients in risk class IV, the odds of death were approximately fivefold higher (odds ratio 5.43, 95% confidence interval: 2.82–10.46) in those admitted to intensive care after a period on the regular ward than in those sent to the intensive care unit directly after surgery. The SAMPE (Anaesthesia and Perioperative Medicine Service) model accurately predicted 30-day postoperative mortality. This model allows identification of high-risk patients and could be used as a practical tool for care stratification and rational postoperative allocation of critical care resources.

Blacks are especially hard hit by cognitive impairment at older ages compared to whites. Here, we take advantage of the Health and Retirement Study (1998-2010) to assess how this racial divide in cognitive impairment is associated with the racial stratification of life course exposures and resources over a 12-year period among 8,946 non-Hispanic whites and blacks ages 65 and older in 1998. We find that blacks suffer from a higher risk of moderate/severe cognitive impairment at baseline and during the follow-up. Blacks are also more likely to report childhood adversity and to have grown up in the segregated South, and these early-life adversities put blacks at a significantly higher risk of cognitive impairment. Adulthood socioeconomic status is strongly associated with the risk of cognitive impairment, net of childhood conditions. However, racial disparities in cognitive impairment, though substantially reduced, are not eliminated when controlling for these life course factors.