Benjamin Cornwell

Just as individuals’ personal social network connections shape their susceptibility to disease, the structure of larger networks in communities shapes the extent of disease spread. We examine how heterogeneity in network structure at the population level affects the spread of disease – namely, COVID-19 – considering varying levels of disease transmissibility and in-person contact rates. Using dynamic simulations that take into account network structure, social contact rates given contextual features of the community (informed by real-life data on family local family structure, schools, workplaces, and daily shopping activities), and disease infection rates, we first confirm that the presence of network hubs and high network degree skewness results in a higher level of infected peak prevalence with infectious diseases such as COVID-19 during periods of low to moderate transmissibility. However, this effect is amplified during lulls in disease spread and is suppressed during periods of greater transmissibility, rendering social network structure more significant during lulls. Moreover, in the case of already highly transmissible diseases, the role of hubs and severe degree skewness is already more continually suppressed.

Just as individuals’ personal social network connections shape their susceptibility to disease, the structure of larger networks in communities shapes the extent of disease spread. We examine how heterogeneity in network structure at the population level affects the spread of disease – namely, COVID-19 – considering varying levels of disease transmissibility and in-person contact rates. Using dynamic simulations that take into account network structure, social contact rates given contextual features of the community (informed by real-life data on family local family structure, schools, workplaces, and daily shopping activities), and disease infection rates, we first confirm that the presence of network hubs and high network degree skewness results in a higher level of infected peak prevalence with infectious diseases such as COVID-19 during periods of low to moderate transmissibility. However, this effect is amplified during lulls in disease spread and is suppressed during periods of greater transmissibility, rendering social network structure more significant during lulls. Moreover, in the case of already highly transmissible diseases, the role of hubs and severe degree skewness is already more continually suppressed.

What is the relationship between inequality and social ties? Do personal networks, group memberships, and connections to social resources help level the playing field, or do they reinforce economic disparities? We examine two core empirical issues: the degree of inequality in social ties and their consolidation with income. Using 142,000 person-wave observations from 15 high-quality U.S. data sets, we measure the quantity and quality of social ties and examine their distribution. Our findings show that (1) the Gini coefficient for social ties often exceeds that of income and (2) social ties are concentrated among those with the highest incomes. We introduce an overall inequality–consolidation curve, demonstrating that social ties generally reinforce economic inequality. However, we identify one key exception: there is no class gradient in the use of social ties for job search. These findings contribute to debates about the role of social ties in perpetuating or mitigating inequality.

The COVID-19 pandemic has affected many aspects of social life, especially among older adults who may face cognitive impairments. Concerning this combination of circumstances, the study evaluates the degree to which data collection on social connectedness among older adults might be affected by the social complexities of the COVID-19 pandemic. We use data from the National Social Life, Health and Aging Project (NSHAP), a nationally representative study of community-dwelling older adults in the U.S., which conducted a special multi-mode COVID study between September 2020 and January 2021, in part to examine social impacts of the COVID-19 pandemic and to assess how alternative survey modes performed during the pandemic. Our final sample includes 2,251 older adults, ages 55 and older. Older adults’ social connectedness was adversely affected by the pandemic. People reported a tendency to move toward electronic communication and away from in-person contact. Concomitantly, there is some evidence of survey mode effects that are related to electronic communication. Those who elected to participate on the phone, or the internet disproportionately reported using those means of communication with their social network members. Notably, this pattern was stronger among those who did not suffer from dementia, suggesting cognition effects on survey completion. Researchers should remain cognizant of how data on social connections were collected during the COVID-19 pandemic. These findings may indicate the role dementia plays in preventing people from adapting to new social networking realities with alternative means of communication during the pandemic.

Research on “the widowhood effect” shows that mortality rates are greater among people who have recently lost a spouse. There are several medical and psychological explanations for this (e.g., “broken heart syndrome”) and sociological explanations that focus on spouses’ shared social-environmental exposures. We expand on sociological perspectives by arguing that couples’ social connections to others play a role in this phenomenon. Using panel data on 1,169 older adults from the National Social Life, Health, and Aging Project, we find that mortality is associated with how well embedded one’s spouse is in one’s own social network. The widowhood effect is greater among those whose spouses were not well connected to one’s other network members. We speculate that the loss of a less highly embedded spouse signals the loss of unique, valuable, nonredundant social resources from one’s network. We discuss theoretical interpretations, alternative explanations, limitations, and directions for future research.

Background: The National Social Life, Health, and Aging Project (NSHAP) has collected 3 rounds of data on older adults’ egocentric social networks. We describe the structure of network data collection for different components of the sample and the data that are available for those groups. We also describe survey techniques that were used to track specific personnel changes that occurred within respondents’ networks during the 10-year study period. Method: Descriptive statistics are presented for measures of network size, composition, and internal structure at all 3 rounds, respondent-level summary measures of change in these characteristics between and across rounds, and measures of change associated with the loss and addition of network members across Rounds 1, 2, and 3. Procedures that were used to clean the network change data are also explained. Results: The NSHAP network change module provides reliable information about specific changes that occurred within respondents’ confidant networks. For returning baseline respondents, there is considerable overlap with respect to which confidants are named in successive rosters, but the norm is for Round 3 networks to be composed primarily of new confidants. Discussion: These data provide new insights into the dynamic nature of networks in later life. Data limitations, and directions for future research, are discussed.

Background: Chest X-ray (CXR) is the first-line test for lung cancer in many settings. Previous research has suggested that higher utilisation of CXR is associated with improved outcomes.Aim: To explore the associations between characteristics of general practices and frequency of investigation with CXR.Design and setting: Retrospective observational study of English general practices.Method: A database was constructed of English general practices containing number of CXRs requested and data on practices for 2018, including patient and staff demographics, smoking prevalence, deprivation, and patient satisfaction indicators. Mixed-effects Poisson modelling was used to account for variation because of chance and to estimate the amount of remaining variation that could be attributed to practice and population characteristics.Results: There was substantial variation in GP CXR rates (median 34 per 1000 patients, interquartile range 26–43). Only 18% of between-practice variance in CXR rate was accounted for by recorded characteristics. Higher practice scores for continuity and communication skills, and higher proportions of smokers, Asian and mixed ethnic groups, and patients aged >65 years were associated with increased CXR rates. Higher patient satisfaction scores for access and greater proportions of male patients and patients of Black ethnicity were associated with lower CXR rates.Conclusion: Substantial variation was found in CXR rates beyond that expected by chance, which could not be accounted for by practices’ recorded characteristics. As other research has indicated that increasing CXR rates can lead to earlier detection, supporting practices that currently investigate infrequently could be an effective strategy to improve lung cancer outcomes.

Objective: We examine whom, among her social network members, a new mother ranks as her most important source of advice for infant care practices and how the ranking of a network member depends on their connectedness to a mother's other network members.Background: Previous research points to the influence of personal networks members' advice on parental practices. However, to design effective interventions that facilitate safe and healthy infant and childcare practices, it is important to understand who, in parents' social circles, they turn to most for advice.Method: We recruited 402 Black and White new mothers in Washington, DC. We analyze the importance rank they assign to their 1,791 network members using nested, dyad‐level rank‐ordered logit models.Results: Connectivity to a mother's other network members and being the mother's mother (i.e., the baby's maternal grandmother) emerged as positive and significant predictors of being ranked as the most important sources of advice. Additionally, the effect of being a grandmother is stronger among grandmothers who are well connected to mothers' other network members.Conclusion: Network members who are well embedded in mothers' own networks are usually viewed by mothers as key sources of advice. Embeddedness is an especially significant condition for new mothers when ranking babies' maternal grandmothers as main advisors.Implications: We encourage future research on mothers' network members as potential targets for infant and childrearing‐related interventions.

Expanding on recent research on the transmission of COVID-19 via social networks, this article argues that exposure to familial and other close contacts who already have the disease may increase the severity of one’s subsequent illness. We hypothesize that having family members or close contacts who were diagnosed with COVID-19 before one’s own diagnosis exacerbates illness severity due to several potential mechanisms including changes in available social support access, increased stress and strain, and increased viral load due to the nature of one’s exposure to the novel coronavirus. We analyze administrative data of all 417 patients who were diagnosed with COVID-19 in the Chinese city of Shenzhen between January 8 and February 25, 2020. Our analyses show that, when patients had family members or close ties diagnosed with COVID-19, they experienced more severe illness. We also find that patients with infected family members or close contacts did not have significantly extended total illness duration, due to their reduced time to diagnosis. The implications of both findings are discussed.