Objectives: The disruption and contraction of older adults’ social networks are among the less discussed consequences of the COVID-19 pandemic. Our objective was to provide an evidence-based commentary on racial/ethnic disparities in social network resources and draw attention to the ways in which disasters differentially affect social networks, with meaningful insight for the ongoing pandemic. Methods: We draw upon prior research on social networks and past natural disasters to identify major areas of network inequality. Attention is given to how pre-pandemic racial/ethnic network disparities are exacerbated during the current crisis, with implications for physical and mental health outcomes. Results: Evidence from the literature shows a robust association between strong social networks and physical and mental health outcomes. During times of crisis, access to social networks for older adults is disrupted, particularly for marginalized groups. We document pre-pandemic disparities in social networks resources and offer insight for examining the impact of COVID-19 on disrupting social networks among older adults. Discussion: Importantly, racial/ethnic disparities in social networks both prior to and as a result of the pandemic intensify existing inequalities and demonstrate the necessity of better understanding social network inequalities for marginalized older adults, particularly in the context of the COVID-19 health crisis.

Network-based intervention strategies can be effective and cost-efficient approaches to curtailing harmful contagions in myriad settings. As studied, these strategies are often impractical to implement, as they typically assume complete knowledge of the network structure, which is unusual in practice. In this paper, we investigate how different immunization strategies perform under realistic conditions—where the strategies are informed by partially-observed network data. Our results suggest that global immunization strategies, like degree immunization, are optimal in most cases; the exception is at very high levels of missing data, where stochastic strategies, like acquaintance immunization, begin to outstrip them in minimizing outbreaks. Stochastic strategies are more robust in some cases due to the different ways in which they can be affected by missing data. In fact, one of our proposed variants of acquaintance immunization leverages a logistically-realistic ongoing survey-intervention process as a form of targeted data-recovery to improve with increasing levels of missing data. These results support the effectiveness of targeted immunization as a general practice. They also highlight the risks of considering networks as idealized mathematical objects: overestimating the accuracy of network data and foregoing the rewards of additional inquiry. It is often useful to track how epidemics spread through populations by mapping transmissions between people, communities, and cities. This consideration of a population as a network can reveal the critical players, locations, or events driving epidemics. Similarly, by mapping the network of possible transmissions before an outbreak occurs, we can identify potentially critical actors on which public health interventions should focus. Unfortunately, the data collection process required to map all possible interactions of a population is difficult— fraught with possible error and unlikely to be complete. To understand the role of data quality in network-based interventions, we apply different strategies to partially-observed networks with controllable amounts of missing data. Our results suggest that intervention strategies which require full network information remain fairly effective up to high levels of missing data. However, local strategies which rely only on small data samples can outperform the more data-expensive benchmark strategies when little data is available. Surprisingly, we also propose an intervention that improves in effectiveness with less data by coupling targeted immunization with targeted data recovery. These results show that insights from network science can be robust to missing data, but that their implementation should be adjusted for noisy real-world applications.

Background: Ecological momentary assessment (EMA) is a set of research methods that capture events, feelings, and behaviors as they unfold in their real-world setting. Capturing data in the moment reduces important sources of measurement error but also generates challenges for noncompliance (ie, missing data). To date, EMA research has only examined the overall rates of noncompliance.Objective: In this study, we identify four types of noncompliance among people who use drugs and aim to examine the factors associated with the most common types.Methods: Data were obtained from a recent pilot study of 28 Nebraskan people who use drugs who answered EMA questions for 2 weeks. We examined questions that were not answered because they were skipped, they expired, the phone was switched off, or the phone died after receiving them.Results: We found that the phone being switched off and questions expiring comprised 93.34% (1739/1863 missing question-instances) of our missing data. Generalized structural equation model results show that participant-level factors, including age (relative risk ratio [RRR]=0.93; P=.005), gender (RRR=0.08; P=.006), homelessness (RRR=3.80; P=.04), personal device ownership (RRR=0.14; P=.008), and network size (RRR=0.57; P=.001), are important for predicting off missingness, whereas only question-level factors, including time of day (ie, morning compared with afternoon, RRR=0.55; P<.001) and day of week (ie, Tuesday-Saturday compared with Sunday, RRR=0.70, P=.02; RRR=0.64, P=.005; RRR=0.58, P=.001; RRR=0.55, P<.001; and RRR=0.66, P=.008, respectively) are important for predicting expired missingness. The week of study is important for both (ie, week 2 compared with week 1, RRR=1.21, P=.03, for off missingness and RRR=1.98, P<.001, for expired missingness).Conclusions: We suggest a three-pronged strategy to preempt missing EMA data with high-risk populations: first, provide additional resources for participants likely to experience phone charging problems (eg, people experiencing homelessness); second, ask questions when participants are not likely to experience competing demands (eg, morning); and third, incentivize continued compliance as the study progresses. Attending to these issues can help researchers ensure maximal data quality.

In the United States, girls and boys have similar science achievement, yet fewer girls aspire to science careers than boys. This paradox emerges in middle school, when peers begin to play a stronger role in shaping adolescent identities. We use complete network data from a single middle school and theories of gender, identity, and social distance to explore how friendship patterns might influence this gender and science paradox. Three patterns highlight the social dimensions of gendered science persistence: (1) boys and girls do not differ in self-perceived science potential and science career aspirations; (2) consistent with gender-based norms, both middle school boys and girls report that the majority of their female friends are not science kinds of people; and (3) youth with gender-inconsistent science aspirations are more likely to be friends with each other than youth with gender normative science aspirations. Together, this evidence suggests that friendship dynamics contribute to gendered patterns in science career aspirations.

Building on previous research that demonstrates the association of youth experiences in afterschool science and higher science identities, this paper presents a network study of 421 middle school students that examines afterschool science participation, friendship ties, and science identities. Participation in afterschool science clubs is associated with higher science identity, but the mechanisms and order of causality are unclear. Youth form friendships inside and outside of school, and peers may influence participation in afterschool activities, as empirical research on friendships shows that they are associated with youth interests. These peer interactions also have the potential to shape identity development during adolescence. In this study, we explore associations among youth participation in afterschool science clubs, peer friendship groups, and science identity. We find that youth who participate in afterschool science clubs have higher science identities than those who do not participate. Additionally, having friends in afterschool science clubs is associated with higher science identity, even among students who report not participating in clubs themselves. Results suggest that afterschool science clubs support youth science identities, even beyond those who directly participate.

Throughout North America, indigenous women experience higher rates of intimate partner violence and sexual violence than any other ethnic group, and so it is of particular importance to understand sources of support for Native American women. In this article, we use social network analysis to study the relationship between social integration and women’s access to domestic violence support by examining the recommendations they would give to another woman in need. We ask two main questions: First, are less integrated women more likely to make no recommendation at all when compared with more socially integrated women? Second, are less integrated women more likely than more integrated women to nominate a formal source of support rather than an informal one? We use network data collected from interviews with 158 Canadian women residing in an indigenous community to measure their access to support. We find that, in general, less integrated women are less likely to make a recommendation than more integrated women. However, when they do make a recommendation, less integrated women are more likely to recommend a formal source of support than women who are more integrated. These results add to our understanding of how access to two types of domestic violence support is embedded in the larger set of social relations of an indigenous community.