The glymphatic system plays an important role in clearing the amyloid-β (Aβ) and tau proteins that are closely linked to Alzheimer disease (AD) pathology. Glymphatic clearance, as well as Aβ accumulation, is highly dependent on sleep, but the sleep-dependent driving forces behind cerebrospinal fluid (CSF) movements essential to the glymphatic flux remain largely unclear. Recent studies have reported that widespread, high-amplitude spontaneous brain activations in the drowsy state and during sleep, which are shown as large global signal peaks in resting-state functional magnetic resonance imaging (rsfMRI), are coupled with CSF movements, suggesting their potential link to glymphatic flux and metabolite clearance. By analyzing multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project, here we showed that the coupling between the global fMRI signal and CSF influx is correlated with AD-related pathology, including various risk factors for AD, the severity of AD-related diseases, the cortical Aβ level, and cognitive decline over a 2-year follow-up. These results provide critical initial evidence for involvement of sleep-dependent global brain activity, as well as the associated physiological modulations, in the clearance of AD-related brain waste.

Social scientists have frequently sought to understand the distinct effects of age, period, and cohort, but disaggregation of the three dimensions is difficult because cohort = period − age. We argue that this technical difficulty reflects a disconnection between how the cohort effect is conceptualized and how it is modeled in the traditional age-period-cohort framework. We propose a new method, called the age-period-cohort-interaction (APC-I) model, that is qualitatively different from previous methods in that it represents Ryder’s theoretical account about the conditions under which cohort differentiation may arise. This APC-I model does not require problematic statistical assumptions and the interpretation is straightforward. It quantifies inter-cohort deviations from the age and period main effects and also permits hypothesis testing about intra-cohort life-course dynamics. We demonstrate how this new model can be used to examine age, period, and cohort patterns in women’s labor force participation.

Random effects (RE) models have been widely used to study the contextual effects of structures such as neighborhoods or schools. The RE approach has recently been applied to age-period-cohort (APC) models that are unidentified because the predictors are exactly linearly dependent. However, research has not fully explained how the RE specification identifies these otherwise unidentified APC models. We address this challenge by first making explicit that RE-APC models have greater—not less—rank deficiency than the traditional fixed-effects model, followed by two empirical examples. We then provide intuition and a mathematical proof to explain that for APC models with one RE, treating one effect as an RE is equivalent to constraining the estimates of that effect’s linear component and the random intercept to be zero. For APC models with two REs, the effective constraints implied by the model depend on the true (i.e., in the data-generating mechanism) nonlinear components of the effects that are modeled as REs, so that the estimated linear components of the REs are determined by the true nonlinear components of those effects. In conclusion, RE-APC models impose arbitrary although highly obscure constraints and thus do not differ qualitatively from other constrained APC estimators.

Intergenerational social mobility has immense implications for individuals’ well-being, attitudes, and behaviors. However, previous methods may be unreliable for estimating heterogeneous mobility effects, especially in the presence of moderate- or large-scale intergenerational mobility. I propose an improved method, called the “mobility contrast model” (MCM). Using simulation evidence, I demonstrate that the MCM is more flexible and reliable for estimating and testing heterogeneous mobility effects, and the results are robust to the scale of intergenerational mobility. I revisit the debate about the effect of mobility on fertility and analyze data from the 1962 Occupational Changes in a Generation Study (OCG-1) and more recent data from the 1974 through 2018 General Social Survey (GSS) using previous models and the MCM. The MCM suggests a small association between fertility and occupational mobility in the GSS data but substantial and heterogeneous educational mobility effects on fertility in the OCG-1 and the GSS. Such effects are difficult to pinpoint using previous methods because mobility effects of different magnitudes and opposite directions among mobility groups may cancel each other out. The new method can be extended to investigate the effect of intergenerational mobility across multiple generations and other research areas, including immigrant assimilation and heterogamy.

Sleep duration can change over the life course; however, previous studies rarely investigated the association between socioeconomic status (SES) and individual sleep trajectories over time. We examined the association between baseline socioeconomic characteristics and long-term sleep trajectories among Black and White adults. This study used data from the Southern Community Cohort Study (N = 45 035). Diverse trajectories of sleep duration were constructed using self-reported sleep duration at baseline and after ~10 years of follow-up. The associations between baseline socioeconomic characteristics and sleep trajectories were examined using multinomial logistic regression. Both Black and White participants experienced similar long-term individual sleep trajectories for baseline educational attainment and employment status albeit the associations appeared stronger among White participants. Lower education and unemployment were associated with higher odds of various suboptimal sleep trajectories suggesting worsening long-term sleep patterns among both racial groups. However, there were some racial differences in the experience of long-term sleep trajectories for household income and neighborhood SES. Household income was notably more important among White than Black individuals; lower household income was associated with higher odds of more suboptimal long-term sleep trajectories for White than Black individuals. Also, neighborhood SES was slightly more important among White than Black individuals; lower neighborhood SES was associated with higher odds of a few suboptimal long-term sleep trajectories for both racial groups. Lower socioeconomic characteristics were associated with various suboptimal long-term sleep trajectories among Black and White participants. Substantial improvements in socio-economic characteristics may contribute to improved sleep patterns.

To investigate the heterogeneous effects of education on sleep duration for Black and White adults and how the education effects changed between 2004 and 2018. A total of 251,994 adult participants in the 2004 to 2018 National Health Interview Survey were included in pooled cross-sectional data analyses. Separately for Black and White men and women, we calculated prevalence ratio and average marginal probability of short sleep (<7 hours) for each education level over the study period based on weighted logistic regression models. Opposite educational gradients in short sleep were observed between Black and White adults. Greater educational attainment was associated with lower likelihood of short sleep among White adults but higher likelihood of short sleep among Black adults. Such heterogeneous educational gradients were robust after accounting for a set of socioeconomic, family, and health factors and persisted between 2004 and 2018. The health implications of education are not uniform in the US population, and heterogeneous education effects on sleep duration persisted over the past decade. More scholarly attention is needed to identify challenges and barriers that may be unique for race, sex, and education subpopulations to maintain healthy sleep.