Populations

East Asian societies have experienced profound changes in family formation, yet comparative evidence on the number of children reported by women across successive surveys remains limited. Using harmonized East Asian Social Survey data from 2006 to 2018, we examine cumulative reported child counts in China, Japan, South Korea, and Taiwan. We describe society–wave patterns, calculate survey-weighted age-standardized means for women aged 25–49, and estimate weighted Poisson models of demographic and socioeconomic correlates. The main analytical sample includes 12,786 women, with additional family-attitude measures available in the 2006 and 2016 surveys. Reported counts generally vary with age and education: women with higher education report fewer children than those in the lowest education category, conditional on observed characteristics. In China, the pronounced difference between unweighted all-age means in 2012 and 2015 is substantially attenuated when comparisons are restricted to ages 25–49 and age-standardized. A positive China-by-later-wave contrast is sensitive to the age range examined and does not identify an effect of fertility-policy reform. These findings highlight the importance of demographic composition and social stratification for understanding cumulative family-size patterns across East Asia.

Stomach cancer remains an important public health concern in the United States. Through investigation of known risk factors, this study aims to map statistically significant geographical differences in mortality indicators for stomach cancer. County-level mortality data (2010–2019) were analyzed using SaTScan with a negative binomial regression to pinpoint purely spatial and space–time clusters of elevated mortality counts and rates. Significant spatial and space–time mortality clusters were identified, with age, physical inactivity, and unemployment among the strongest predictors. By procuring key evidence on the spatial and temporal dispersal of deaths due to stomach cancer in the United States, this study brings to light geographic disparities which warrant targeted public health interventions.

Lifespan varies among individuals, and this variation arises both from heterogeneity (e.g., in sex or race) and from unavoidable individual stochasticity. Treating a heterogeneous population as a mixture of groups, each experiencing different rates, makes it possible to partition lifespan variance into a between-group component due to heterogeneity and a within-group component due to chance. Until now, such studies could only treat factors one at a time. This paper presents an analysis that applies to multi-factor studies and quantifies the contribution of multiple factors and their interactions to variance in lifespan. As a case study it is applied to life table data from a four-factor (sex, marital status, education, and race) study of longevity in the U.S. population, 2015–2019. Even accounting for four main factors, six two-way interactions, four three-way interactions, and one four-way interaction, between-group heterogeneity accounts for only 7% to 10% of lifespan variance. Education and its interactions make the largest contribution. Main effects make the largest contributions; those of two-way, three-way, and four-way interactions are orders of magnitude smaller.