The coronavirus disease 2019 (COVID-19) pandemic has led to a large increase in mortality in the United States and around the world, leaving many grieving the sudden loss of family members. We created an indicator—the COVID-19 bereavement multiplier—that estimates the average number of individuals who will experience the death of a close relative (defined as a grandparent, parent, sibling, spouse, or child) for each COVID-19 death. Using demographic microsimulation-based estimates of kinship networks in the United States, the clear age gradient in COVID-19 mortality seen across contexts, and several hypothetical infection prevalence scenarios, we estimate COVID-19 bereavement multipliers for White and Black individuals in the United States. Our analysis shows that for every COVID-19 death, approximately nine surviving Americans will lose a grandparent, parent, sibling, spouse, or child. These estimates imply, for example, that if 190,000 Americans die from COVID-19, as some models project, then ∼1.7 million will experience the death of a close relative. We demonstrate that our estimates of the bereavement multiplier are stable across epidemiological realities, including infection scenarios, total number of deaths, and the distribution of deaths, which means researchers can estimate the bereavement burden over the course of the epidemic in lockstep with rising death tolls. In addition, we provide estimates of bereavement multipliers by age group, types of kin loss, and race to illuminate prospective disparities. The bereavement multiplier is a useful indicator for tracking COVID-19’s multiplicative impact as it reverberates across American families and can be tailored to other causes of death.

In this article, we make the case that social epidemiology provides a useful framework to define the environment within gene-environment (G·E) research. We describe the environment in a multilevel, multidomain, longitudinal framework that accounts for upstream processes influencing health outcomes. We then illustrate the utility of this approach by describing how intermediate levels of social organization, such as neighborhoods or schools, are key environmental components of G·E research. We discuss different models of G·E research and encourage public health researchers to consider the value of including genetic information from their study participants. We also encourage researchers interested in G·E interplay to consider the merits of the social epidemiology model when defining the environment. (Am J Public Health.

During the transition to adulthood, many unhealthy behaviors are developed that in turn shape behaviors, health, and mortality in later life. However, research on unhealthy behaviors and risky transitions has mostly focused on one health problem at a time. In this article, we examine variation in health behavior trajectories, how trajectories cluster together, and how the likelihood of experiencing different behavior trajectories varies by sociodemographic characteristics. We use the National Longitudinal Study of Adolescent Health (Add Health) Waves I to IV to chart the most common health behavior trajectories over the transition to adulthood for cigarette smoking, alcohol consumption, obesity, and sedentary behavior. We find that health behavior trajectories cluster together in seven joint classes and that sociodemographic factors (including gender, parental education, and race-ethnicity) significantly predict membership in these joint trajectories.

Kinship networks are important but remain understudied in contemporary developed societies. Because hazards of vital events such as marriage, fertility, and mortality vary demographically, it is likely that average numbers of extended kin also vary meaningfully by education and race, but researchers have not addressed this topic. Existing research on kinship in developed societies focuses on group-level differences in multiplex kin networks such as those comprising household co-residence, instrumental and emotional support, and frequency of contact. By contrast, we provide the first population-based estimates of group-level differences in living kin in the contemporary United States. We estimate, by race, educational attainment, and age, average numbers of living parents, children, spouse/partner, full and half siblings, grandparents, grandchildren, aunt/uncles, nieces/nephews, and cousins, and test whether group differences in average kin counts are attributable to group differences in kin mortality and other processes.

Many unhealthy behaviors develop during adolescence, and these behaviors can have fundamental consequences for health and mortality in adulthood. Social network structure and the degree of homophily in a network affect how health behaviors and innovations are spread. However, the degree of health behavior homophily across different social ties and within subpopulations is unknown. This paper addresses this gap in the literature by using a novel regression model to document the degree of homophily across various relationship types and subpopulations for behaviors of interest that are related to health outcomes. These patterns in health behavior homophily have implications for which behaviors and ties should be the subjects of future research and for predicting how homophily may shape health programs focused on specific subpopulations (gender, race, class, health status) or a specific social context (families, peer groups, classrooms, or school activities).

We use genome wide data from respondents of the Health and Retirement Study (HRS) to evaluate the possibility that common genetic influences are associated with education and three health outcomes: depression, self-rated health, and body mass index. We use a total of 1.7 million single nucleotide polymorphisms obtained from the Illumina HumanOmni2.5-4v1 chip from 4,233 non-Hispanic white respondents to characterize genetic similarities among unrelated persons in the HRS. We then used the Genome Wide Complex Trait Analysis (GCTA) toolkit, to estimate univariate and bivariate heritability. We provide evidence that education (h2 = .33), BMI (h2 = .43), depression (h2 = .19), and self-rated health (h2 = .18) are all moderately heritable phenotypes. We also provide evidence that some of the correlation between depression and education as well as self-rated health and education is due to common genetic factors associated with one or both traits. We find no evidence that the correlation between education and BMI is influenced by common genetic factors.

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