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Background In the field of cardiovascular risk assessment, aortic stiffness is considered a key predictive indicator, and carotid-femoral pulse wave velocity (cfPWV) is recognized as the gold standard for non-invasive assessment of atherosclerotic risk in the aorta. Due to challenges such as technical difficulty, cfPWV testing has not been widely implemented in China. Objective This study aimed to develop and validate a screening model for early atherosclerotic risk in the aorta based on cardiovascular risk factors, with the intention of replacing the complex measurement process of cfPWV and reducing reliance on traditional measurement methods. Methods A total of 878 participants recruited from the Health Checkup Center of the First Affiliated Hospital of Anhui Medical University between May and November 2023 were selected as research subjects, randomly divided into a model-building group (n=703) and a validation group (n=175) in an 8∶2 ratio. Patient general information, laboratory test results, and cfPWV were collected. Based on the cfPWV examination results and relevant guidelines, participants in the model-building group were divided into those without atherosclerotic risk in the aorta (n=503) and those with atherosclerotic risk in the aorta (n=200). Multifactorial Logistic regression analysis was used to screen variables and establish a nomogram assessment model. The receiver operating characteristic curve (ROC curve) for predicting the risk of atherosclerosis in the aorta was plotted for the model, and the model's discriminative ability and calibration were assessed using the area under the ROC curve (AUC) and the Hosmer-Lemeshow test, respectively. The Delong test was used to compare the AUCs of different models, and decision curve analysis (DCA) was used to assess the clinical utility of the model. Internal validation of the model was performed using the bootstrap method with 1 000 resampling iterations. Results Participants with atherosclerotic risk in the model-building group were older, had higher BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), urea, fasting blood glucose (FBG), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), total cholesterol (TC), alanine aminotransferase (ALT), aspartate aminotransferase (AST), hemoglobin (Hb), and a higher proportion of alcohol consumption, dyslipidemia, and diabetes than those without atherosclerotic risk in the aorta. The glomerular filtration rate (GFR) and platelet count (PLT) were lower in those with atherosclerotic risk (P0.05). A nomogram model was constructed using age, MAP, FBG, and Hb as predictive factors, with an AUC of 0.941 (95%CI=0.920-0.962) for the training set, sensitivity of 0.832, and specificity of 0.917. The AUC for the validation set was 0.961 (95%CI=0.914-1.000), with sensitivity of 0.872 and specificity of 0.964. DCA results indicated that the use of the early atherosclerosis screening model could benefit participants in clinical practice. Conclusion Based on four simple indexes of age, mean arterial pressure, hemoglobin and fasting blood glucose, a screening model for early aortic sclerosis risk was established, which provides a convenient and efficient method for early vascular function screening.

One of the most important developments in the current era of social sciences is the growing availability and diversity of data, big and small. Social scientists increasingly combine information from multiple data sets in their research. While conducting statistical analyses with linked data is relatively straightforward, borrowing information across unlinked data can be much more challenging due to the absence of unit-to-unit linkages. This article proposes a new methodological approach for borrowing information across "unlinked" surveys to predict unobserved distributions. The gist of the proposed approach lies in the idea of using the "relative density" between the observed and unobserved distributions in the reference data to characterize the difference between the two distributions and borrow that information to the base data. Relying on the assumption that the relative density between the observed and unobserved distributions is similar between data sets, the proposed relative density approach has the key advantage of allowing the researcher to borrow information about the shape of the distribution, rather than a few summary statistics. The approach also comes with a method for incorporating and quantifying the uncertainty in its output. We illustrate the formulation of this approach, demonstrate with simulation examples, and finally apply it to address the problem of employment selection in wage inequality research.

College graduates earn higher wages than high school graduates by age 30. Among women, the advantages of a college degree decline somewhat as they age, although they are still substantial at age 50; for men, the advantage of a college degree grows throughout the life cycle. Most previous research on returns to higher education has focused on income at a single point in time or averaged over multiple years; our contribution is to study how returns vary by age. We also document how these patterns vary by the propensity of graduating from college. We find modest wage returns for mid-propensity college graduates, but large returns for low-propensity and, for men, high-propensity college graduates. Our results rely on propensity score–based matching combined with multilevel growth curve models applied to data from the National Longitudinal Survey of Youth 1979 cohort.

BackgroundChildhood family income variation is an understudied aspect of households' economic context that may have distinct consequences for children. We identified trajectories of childhood family income over a 12-year period, and examined associations between these trajectories and later psychiatric disorders, among individuals born in Sweden between 1987 and 1991 (n=534 294).MethodsWe used annual income data between the ages of 3-14 years and identified 5 trajectories (2 high-income upward, 1 downward and 2 low-income upward trajectories). Psychiatric disorders in the follow-up period after age 15 were defined from International Classification of Disease (ICD)-codes in a nationwide patient register. Multiadjusted risks for all psychiatric disorders, as well as for specific psychiatric diagnoses, were calculated as HRs with 95% CIs.ResultsOf the 5 identified income trajectories, the constant low and the downward trajectories were particularly associated with later psychiatric disorder. Children with these trajectories had increased risks for psychiatric disorder, including mood, anxiety, psychotic disorders and attention deficit/hyperactivity disorder. The association remained, even after adjusting for important variables including parental psychiatric disorder. In contrast, the relationship was reversed for eating disorders, for which children in higher income trajectories had elevated risks.ConclusionsFindings show that children growing up in a household characterised by low or decreasing family income have an increased risk for psychiatric disorder. Continued work is needed to reduce socioeconomic inequalities in psychiatric disorders. Policies and interventions for psychiatric disorders should consider the socioeconomic background of the family as an important risk or protective factor.

Partisan sorting in residential environments is an enduring feature of contemporary American politics, but little research has examined partisan segregation individuals experience in activity spaces through their daily activities. Relying on advances in spatial computation and global positioning system data on everyday mobility flows collected from smartphones, we measure experienced partisan segregation in two ways: place-level partisan segregation based on the partisan composition of its daily visitors and community-level experienced partisan segregation based on the segregation level of places visited by its residents. We find that partisan segregation experienced in places varies across different geographic areas, location types, and time periods. Moreover, partisan segregation is distinct from experienced segregation by race and income. We also find that partisan segregation individuals experience is relatively lower when they visit places beyond their residential areas, but partisan segregation in residential space and activity space is strongly correlated. Residents living in predominantly black, liberal, low-income, non-immigrant, more public transit-dependent, and central city communities tend to experience a higher level of partisan segregation. [ABSTRACT FROM AUTHOR]