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.

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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).

The application of a currently proposed differential privacy algorithm to the 2020 United States Census data and additional data products may affect the usefulness of these data, the accuracy of estimates and rates derived from them, and critical knowledge about social phenomena such as health disparities. We test the ramifications of applying differential privacy to released data by studying estimates of US mortality rates for the overall population and three major racial/ethnic groups. We ask how changes in the denominators of these vital rates due to the implementation of differential privacy can lead to biased estimates. We situate where these changes are most likely to matter by disaggregating biases by population size, degree of urbanization, and adjacency to a metropolitan area. Our results suggest that differential privacy will more strongly affect mortality rate estimates for non-Hispanic blacks and Hispanics than estimates for non-Hispanic whites. We also find significant changes in estimated mortality rates for less populous areas, with more pronounced changes when stratified by race/ethnicity. We find larger changes in estimated mortality rates for areas with lower levels of urbanization or adjacency to metropolitan areas, with these changes being greater for non-Hispanic blacks and Hispanics. These findings highlight the consequences of implementing differential privacy, as proposed, for research examining population composition, particularly mortality disparities across racial/ethnic groups and along the urban/rural continuum. Overall, they demonstrate the challenges in using the data products derived from the proposed disclosure avoidance methods, while highlighting critical instances where scientific understandings may be negatively impacted.

This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of variance estimation for the construction of confidence intervals and hypothesis tests. In this paper, we show that the estimators of RDS sampling variance rely on a critical assumption that the network is First Order Markov (FOM) with respect to the dependent variable of interest. We demonstrate, through intuitive examples, mathematical generalizations, and computational experiments that current RDS variance estimators will always underestimate the population sampling variance of RDS in empirical networks that do not conform to the FOM assumption. Analysis of 215 observed university and school networks from Facebook and Add Health indicates that the FOM assumption is violated in every empirical network we analyze, and that these violations lead to substantially biased RDS estimators of sampling variance. We propose and test two alternative variance estimators that show some promise for reducing biases, but which also illustrate the limits of estimating sampling variance with only partial information on the underlying population social network.

We address a long hypothesized relationship between the proximity of individuals' dwelling units and their kinship association. Better understanding this relationship is important because of its implications for contact and association among members of a society. In this paper, we use a unique dataset from Nang Rong, Thailand which contains dwelling unit locations (GPS) and saturated kinship networks of all individuals living in 51 agricultural villages. After presenting arguments for a relationship between individuals’ dwelling unit locations and their kinship relations as well as the particulars of our case study, we introduce the data and describe our analytic approach. We analyze how kinship - considered as both a system linking collections of individuals in an extended kinship network and as dyadic links between pairs of individuals -patterns the proximity of dwelling units in rural villages. The results show that in general, extended kin live closer to one another than do unrelated individuals. Further, the degree of relatedness between kin correlates with the distance between their dwelling units. Close kin are more likely to co-reside, a fact which drives much of the relationship between kinship relatedness and dwelling unit proximity within villages. There is nevertheless suggestive evidence of a relationship between kinship association and dwelling unit proximity among kin who do not live together.

Scholars of transnationalism have argued that migrants create transnational social fields or spaces that connect their place of origin to destination areas. Despite the centrality that social networks have in the definition of these concepts, quantitative and mixed-methods social network research is rare in research on transnationalism. This situation, however, has changed over the last decade, and the transnational social networks of migrants have been studied with multiple methodologies. So far, this literature has not been systematically evaluated. With the aim of taking stock of this research, we classify the literature into four types of approaches (individual, household, dyad/small set, and community) and review their distinct contributions regarding the functioning of immigrants’ transnational networks, as well as the relative strengths and limitations of each approach. On the basis of our analysis, we discuss pathways for future investigation.