Social capital I: measurement and associations with economic mobility

Social Class and Social Pain: Target SES Biases Judgments of Pain and Support for White Target Individuals

Social pain, defined as distress caused by negative interpersonal experiences (e.g., ostracism, mistreatment), is detrimental to health. Yet, it is unclear how social class might shape judgments of the social pains of low-socioeconomic status (SES) and high-SES individuals. Five studies tested competing toughness and empathy predictions for SES's effect on social pain judgments. Consistent with an empathy account, in all studies (Ncumulative = 1,046), low-SES White targets were judged more sensitive to social pain than high-SES White targets. Further, empathy mediated these effects, such that participants felt greater empathy and expected more social pain for low-SES targets relative to high-SES targets. Social pain judgments also informed judgments of social support needs, as low-SES targets were presumed to need more coping resources to manage hurtful events than high-SES targets. The current findings provide initial evidence that empathic concern for low-SES White individuals sensitizes social pain judgments and increases expected support needs for lower class White individuals.

The Emerging Science of Interacting Minds

For over a century, psychology has focused on uncovering mental processes of a single individual. However, humans rarely navigate the world in isolation. The most important determinants of successful development, mental health, and our individual traits and preferences arise from interacting with other individuals. Social interaction underpins who we are, how we think, and how we behave. Here we discuss the key methodological challenges that have limited progress in establishing a robust science of how minds interact and the new tools that are beginning to overcome these challenges. A deep understanding of the human mind requires studying the context within which it originates and exists: social interaction.

A greening theory of change: How neighborhood greening impacts adolescent health disparities

Neighborhoods are one of the key determinants of health disparities among young people in the United States. While neighborhood deprivation can exacerbate health disparities, amenities such as quality parks and greenspace can support adolescent health. Existing conceptual frameworks of greening-health largely focus on greenspace exposures, rather than greening interventions. In this paper, we develop and propose a Greening Theory of Change that explains how greening initiatives might affect adolescent health in deprived neighborhoods. The theory situates greening activities and possible mechanisms of change in the context of their ability to modify distal social determinants of health factors, stemming from macrostructural and historical processes that lead to resource inequalities, affecting both the social and built environment in which adolescents live and develop. The framework illustrates both short- and long-term health, economic, and security effects of greening. We also describe how the theory informed the development of Project VITAL (Vacant lot Improvement to Transform Adolescent Lives) in Baltimore, MD, which aims to (1) build a citywide sharable database on vacant lot restoration activities, (2) evaluate the impact of greening initiatives on adolescent health outcomes, (3) conduct cost-effectiveness analyses, and (4) develop best practices for greening programs for improved adolescent health.

The unequal landscape of civic opportunity in America

The hollowing of civil society has threatened effective implementation of scientific solutions to pressing public challenges-which often depend on cultivating pro-social orientations commonly studied under the broad umbrella of social capital. Although robust research has studied the constituent components of social capital from the demand side (that is, the orientations people need for collective life in pluralistic societies, such as trust, cohesion and connectedness), the same precision has not been brought to the supply side. Here we define the concept of civic opportunity-opportunities people have to encounter civic experiences necessary for developing such orientations-and harness data science to map it across America. We demonstrate that civic opportunity is more highly correlated with pro-social outcomes such as mutual aid than other measures, but is unequally distributed, and its sources are underrepresented in the public dialogue. Our findings suggest greater attention to this fundamentally uneven landscape of civic opportunity.

Modification of Firearm Law-Firearm Injury Association by Economic Disadvantage

INTRODUCTION

Firearm-related injuries in the U.S. have risen 37% since 2015. Understanding how the association between firearm incidents and state-level firearm restrictiveness is modified by community-level distress and economic connectedness (EC) may inform upstream injury prevention efforts.

METHODS

A national cross-sectional study of firearm incidents (interpersonal and unintentional firearm events) occurring between 1/2015 and 12/2021 was performed using the Gun Violence Archive. The exposures were community distress (Distressed Communities Index, DCI), EC, and year-state-level firearm restrictiveness. The primary outcome was mean annual urban firearm incidence rate per ZIP Code Tabulation Area. Generalized linear mixed models were fit to evaluate the modification of the firearm law-firearm incident association by DCI and EC. Data analyses took place in 2022.

RESULTS

About 266,020 firearm incidents were included. The mean rate was higher with each DCI tertile, with a RR of 3.18 (95% CI: 3.06, 3.30) in high versus low distress communities. Low EC was associated with over 1.8 times greater rate of firearm-related injury. The least restrictive firearm laws were associated with 1.20 times higher risk of firearm incidents (95% CI: 1.12, 1.28). The association between restrictive laws and lower incidence rates was strongest in low and medium distress and high EC communities.

CONCLUSIONS

Stricter firearm laws are associated with lower rate of firearm incidents. The magnitude of this association is smallest for communities experiencing the greatest economic disadvantage.

Effect of social capital, social support and social network formation on the quality of life of American adults during COVID-19

This study aims to evaluate the effect of social capital (SC), social support (SS), and social network formation (SNF) on the quality of life of American adults during COVID-19. Using a probability sample of American adults aged 49+, 2370 respondents were selected from the National Social Life Health and Aging Project (NSHAP) dataset for analysis using an integrated partial least squares based on structural equation modeling (PLS-SEM)-K-fold cross-validation approach. The analysis showed that social capital assessed using civic engagement, social cohesion, socioeconomic status (SES), social support, and social network formation were significantly and positively associated with American adults' quality of life during the COVID-19 pandemic. Furthermore, the results showed that using the PLS-SEM and K-fold cross-validation approach produced a medium predictive power of the overall model, confirming the importance of SC, SS, and SNF in predicting quality of life-outcomes. These findings suggest that efforts to promote the well-being of American adults, especially older adults, during the pandemic should focus on strengthening social capital, social support and social network formation.

A systematic review of how social connectedness influences associations between racism and discrimination on health outcomes

Racial discrimination is a well-known risk factor of racial disparities in health. Although progress has been made in identifying multiple levels through which racism and racial discrimination influences health, less is known about social factors that may buffer racism's associations with health. We conducted a systematic review of the literature with a specific focus on social connectedness, racism, and health, retrieving studies conducted in the United States and published between January 1, 2012, and July 30, 2022, in peer-reviewed journals. Of the 787 articles screened, 32 were selected for full-text synthesis. Most studies (72%) were at the individual level, cross-sectional, and among community/neighborhood, school, or university samples. Studies had good methodological rigor and low risk of bias. Measures of racism and racial discrimination varied. Discrimination scales included unfair treatment because of race, schedule of racist events, experiences of lifetime discrimination, and everyday discrimination. Measures of social connectedness (or disconnectedness) varied. Social-connectedness constructs included social isolation, loneliness, and social support. Mental health was the most frequently examined outcome (75%). Effect modification was used in 56% of studies and mediation in 34% of studies. In 81% of studies, at least 1 aspect of social connectedness significantly buffered or mediated the associations between racism and health. Negative health associations were often weaker among people with higher social connectedness. Social connectedness is an important buffering mechanism to mitigate the associations between racial discrimination and health. In future studies, harmonizing metrics of social connectedness and racial discrimination can strengthen causal claims to inform interventions.

Human mobility networks reveal increased segregation in large cities

A long-standing expectation is that large, dense and cosmopolitan areas support socioeconomic mixing and exposure among diverse individuals1-6. Assessing this hypothesis has been difficult because previous measures of socioeconomic mixing have relied on static residential housing data rather than real-life exposures among people at work, in places of leisure and in home neighbourhoods7,8. Here we develop a measure of exposure segregation that captures the socioeconomic diversity of these everyday encounters. Using mobile phone mobility data to represent 1.6 billion real-world exposures among 9.6 million people in the United States, we measure exposure segregation across 382 metropolitan statistical areas (MSAs) and 2,829 counties. We find that exposure segregation is 67% higher in the ten largest MSAs than in small MSAs with fewer than 100,000 residents. This means that, contrary to expectations, residents of large cosmopolitan areas have less exposure to a socioeconomically diverse range of individuals. Second, we find that the increased socioeconomic segregation in large cities arises because they offer a greater choice of differentiated spaces targeted to specific socioeconomic groups. Third, we find that this segregation-increasing effect is countered when a city's hubs (such as shopping centres) are positioned to bridge diverse neighbourhoods and therefore attract people of all socioeconomic statuses. Our findings challenge a long-standing conjecture in human geography and highlight how urban design can both prevent and facilitate encounters among diverse individuals.

Strangers in a strange land: mapping household and neighbourhood associations with improved wellbeing outcomes in Accra, Ghana

Urban poverty is not limited to informal settlements, rather it extends throughout cities, with the poor and affluent often living in close proximity. Using a novel dataset derived from the full Ghanaian Census, we investigate how neighbourhood versus household socio-economic status (SES) relates to a set of household development outcomes (related to housing quality, energy, water and sanitation, and information technology) in Accra, Ghana. We then assess "stranger" households' outcomes within neighbourhoods: do poor households fare better in affluent neighbourhoods, and are affluent households negatively impacted by being in poor neighbourhoods? Through a simple generalized linear model we estimate the variance components associated with household and neighbourhood status for our outcome measures. Household SES is more closely associated with 13 of the 16 outcomes assessed compared to the neighbourhood average SES. Second, for 9 outcomes poor households in affluent areas fair better, and the affluent in poor areas are worse off. For two outcomes, poor households have worse outcomes in affluent areas, and the affluent have better outcomes in poor areas, on average. For three outcomes "stranger" households do worse in strange neighbourhoods. We discuss implications for mixed development and how to direct resources through households versus location-based targets.

Emergency communications after earthquake reveal social network backbone of important ties

Social networks provide a basis for collective resilience to disasters. Combining the quasi-experimental context of a major earthquake in Ya'an, China, with anonymized mobile telecommunications records regarding 91,839 Ya'an residents, we use initial bursts of postdisaster communications (e.g. choice of alter, order of calls, and latency) to reveal the "important ties" that form the social network backbone. We find that only 26.8% of important ties activated during the earthquake were the strongest ties during normal times. Many important ties were hitherto latent and weak, only to become persistent and strong after the earthquake. We show that which ties activated during a sudden disaster are best predicted by the interaction of embeddedness and tie strength. Moreover, a backbone of important ties alone (without the inclusion of weak ties ordinarily seen as important to bridge communities) is sufficient to generate a hierarchical structure of social networks that connect a disaster zone's disparate communities.

An in-depth examination of requirements for disclosure risk assessment

The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial Census of Population and Housing has triggered renewed interest and debate over how to measure the disclosure risks and societal benefits of the published data products. We argue that any proposal for quantifying disclosure risk should be based on prespecified, objective criteria. We illustrate this approach to evaluate the absolute disclosure risk framework, the counterfactual framework underlying differential privacy, and prior-to-posterior comparisons. We conclude that satisfying all the desiderata is impossible, but counterfactual comparisons satisfy the most while absolute disclosure risk satisfies the fewest. Furthermore, we explain that many of the criticisms levied against differential privacy would be levied against any technology that is not equivalent to direct, unrestricted access to confidential data. More research is needed, but in the near term, the counterfactual approach appears best-suited for privacy versus utility analysis.

Family, Community, and the Rural Social Mobility Advantage

Children born into poverty in rural America achieve higher average income levels as adults than their urban peers. As economic opportunity tends to be more abundant in cities, this "rural advantage" in income mobility seems paradoxical. This article resolves this puzzle by applying multilevel analysis to new spatial measures of rurality and place-level data on intergenerational income mobility. We show that the high level of rural income mobility is principally driven by boys of rural-origin, who are more likely than their urban peers to grow up in communities with a predominance of two-parent households. The rural advantage is most pronounced among Whites and Hispanics, as well as those who were raised in the middle of the country. However, these dynamics are more nuanced for girls. In fact, girls from lower-income rural households exhibit a disadvantage in their personal income attainment, partly due to the persistence of traditional gender norms. These findings underscore the importance of communities with strong household and community supports in facilitating later-life income mobility, particularly for boys. They also challenge the emerging consensus that attributes the rural income mobility advantage to migration from poorer rural areas to wealthier towns and cities.

The Exposome and Cardiovascular Health

The study of the interplay between social factors, environmental hazards, and health has garnered much attention in recent years. The term "exposome" was coined to describe the total impact of environmental exposures on an individual's health and well-being, serving as a complementary concept to the genome. Studies have shown a strong correlation between the exposome and cardiovascular health, with various components of the exposome having been implicated in the development and progression of cardiovascular disease. These components include the natural and built environment, air pollution, diet, physical activity, and psychosocial stress, among others. This review provides an overview of the relationship between the exposome and cardiovascular health, highlighting the epidemiologic and mechanistic evidence of environmental exposures on cardiovascular disease. The interplay between various environmental components is discussed, and potential avenues for mitigation are identified.

The interdependence of relational and material wealth inequality in Pemba, Zanzibar

The extent of inequality in material wealth across different types of societies is well established. Less clear, however, is how material wealth is associated with relational wealth, and the implications of such associations for material wealth inequality. Theory and evidence suggest that material wealth both guides, and is patterned by, relational wealth. While existing comparative studies typically assume complementarity between different types of wealth, such associations may differ for distinct kinds of relational wealth. Here, we first review the literature to identify how and why different forms of relational wealth may align. We then turn to an analysis of household-level social networks (food sharing, gender-specific friendship and gender-specific co-working networks) and material wealth data from a rural community in Pemba, Zanzibar. We find that (i) the materially wealthy have most relational ties, (ii) the associations between relational and material wealth-as well as relational wealth more generally-are patterned by gender differences, and (iii) different forms of relational wealth have similar structural properties and are closely aligned. More broadly, we show how examining the patterning of distinct types of relational wealth provides insights into how and why inequality in material wealth remains muted in a community undergoing rapid economic change. This article is part of the theme issue 'Evolutionary ecology of inequality'.

Community Social Capital and Population Health Outcomes

Importance

While the association between economic connectedness and social mobility has now been documented, the potential linkage between community-level economic connectedness and population health outcomes remains unknown.

Objective

To examine the association between community social capital measures (defined as economic connectedness, social cohesion, and civic engagement) and population health outcomes (defined across prevalence of diabetes, hypertension, high cholesterol, kidney disease, and obesity).

Design, Setting, and Participants

This cross-sectional study included communities defined at the zip code tabulation area (ZCTA) level in all 50 US states. Data were collected from January 2021 to December 2022.

Main Outcomes and Measures

Multivariable regression analyses were used to examine the association between population health outcomes and social capital. Adjusted analyses controlled for area demographic variables and county fixed effects. Heterogeneities within the associations based on the racial and ethnic makeup of communities were also examined.

Results

In this cross-sectional study of 17 800 ZCTAs, across 50 US states, mean (SD) economic connectedness was 0.88 (0.32), indicating friendship sorting on income; the mean (SD) support ratio was 0.90 (0.10), indicating that 90% of ties were supported by a common friendship tie; and the mean (SD) volunteering rate was 0.08 (0.03), indicating that 8% of individuals within a given community were members of volunteering associations. Mean (SD) ZCTA diabetes prevalence was 10.8% (2.9); mean (SD) high blood pressure prevalence was 33.2% (6.2); mean (SD) high cholesterol prevalence was 32.7% (4.2), mean (SD) kidney disease prevalence was 3.0% (0.7), and mean (SD) obesity prevalence was 33.4% (5.6). Regression analyses found that a 1% increase in community economic connectedness was associated with significant decreases in prevalence of diabetes (-0.63%; 95% CI, -0.67% to -0.60%); hypertension (-0.31%; 95% CI, -0.33% to -0.29%); high cholesterol (-0.14%; 95% CI, -0.15% to -0.12%); kidney disease (-0.48%; 95% CI, -0.50% to -0.46%); and obesity (-0.28%; 95% CI, -0.29% to -0.27%). Second, a 1% increase in the community support ratio was associated with significant increases in prevalence of diabetes (0.21%; 95% CI, 0.16% to 0.26%); high blood pressure (0.16%; 95% CI, 0.13% to 0.19%); high cholesterol (0.16%; 95% CI, 0.13% to 0.19%); kidney disease (0.17%; 95% CI, 0.13% to 0.20%); and obesity (0.08%; 95% CI, 0.06% to 0.10%). Third, a 1% increase in the community volunteering rate was associated with significant increases in prevalence of high blood pressure (0.02%; 95% CI, 0.01% to 0.02%); high cholesterol (0.03%; 95% CI, 0.02% to 0.03%); and kidney disease (0.02%; 95% CI, 0.01% to 0.02%). Additional analyses found that the strength of these associations varied based on the majority racial and ethnic population composition of communities.

Conclusions and Relevance

In this study, higher economic connectedness was significantly associated with better population health outcomes; however, higher community support ratios and volunteering rates were both significantly associated with worse population health. Associations also differed by majority racial and ethnic composition of communities.

Does social capital matter? A study of hit-and-run in US counties

We investigate the relationship between social capital and a decision that has dire health consequences: fleeing after a road accident. This event is unplanned, and the decision is taken under great emotional distress and time pressure, thus providing a test of whether social capital matters for behaviour in extreme conditions. We merge data from the universe of fatality accidents involving pedestrians in the US over the period 2000-2018 with a dataset on social capital measures at the county level. Using within-state-year variation, our results show that one standard deviation increase in social capital is associated with a reduction in the probability of hit-and-run of around 10.5%. Several falsification tests based on differences in social capital endowment between the county where the accident occurs and the county where the driver resides are suggestive of a causal interpretation of this evidence. Our findings show the importance of social capital in a new context, suggesting a broad impact on pro-social behaviour and adding to the positive returns of promoting civic norms.

The anatomy of a population-scale social network

Large-scale human social network structure is typically inferred from digital trace samples of online social media platforms or mobile communication data. Instead, here we investigate the social network structure of a complete population, where people are connected by high-quality links sourced from administrative registers of family, household, work, school, and next-door neighbors. We examine this multilayer social opportunity structure through three common concepts in network analysis: degree, closure, and distance. Findings present how particular network layers contribute to presumably universal scale-free and small-world properties of networks. Furthermore, we suggest a novel measure of excess closure and apply this in a life-course perspective to show how the social opportunity structure of individuals varies along age, socio-economic status, and education level.

How Did Reskilling During the COVID-19 Pandemic Relate to Entrepreneurship and Optimism? Barriers, Opportunities, and Implications for Equity

With shorter durations and fewer barriers to entry, reskilling programs may serve as vehicles for social mobility and equity, as well as tools for creating a more adaptive workforce and inclusive economy. Nevertheless, much of the limited large-scale research on these types of programs was conducted prior to the COVID-19 pandemic. Thus, given the social and economic disruptions spurred by the pandemic, our ability to understand the impact of these types of programs in recent labor market conditions is limited. We fill this gap by leveraging three waves of a longitudinal household financial survey collected across all 50 US states during the pandemic. Through descriptive and inferential methods, we explore the sociodemographic characteristics related to reskilling and associated motivations, facilitators, and barriers, as well as the relationships between reskilling and measures of social mobility. We find that reskilling is positively related to entrepreneurship and, for Black respondents, to optimism. Moreover, we find that reskilling is not merely a tool for upward social mobility, but also economic stability. However, our results demonstrate that reskilling opportunities are stratified across race/ethnicity, gender, and socioeconomic status through both formal and informal mechanisms. We close with a discussion of implications for policy and practice.

Crime, inequality and public health: a survey of emerging trends in urban data science

Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale.

Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics

Urbanization and its problems require an in-depth and comprehensive understanding of urban dynamics, especially the complex and diversified lifestyles in modern cities. Digitally acquired data can accurately capture complex human activity, but it lacks the interpretability of demographic data. In this paper, we study a privacy-enhanced dataset of the mobility visitation patterns of 1.2 million people to 1.1 million places in 11 metro areas in the U.S. to detect the latent mobility behaviors and lifestyles in the largest American cities. Despite the considerable complexity of mobility visitations, we found that lifestyles can be automatically decomposed into only 12 latent interpretable activity behaviors on how people combine shopping, eating, working, or using their free time. Rather than describing individuals with a single lifestyle, we find that city dwellers' behavior is a mixture of those behaviors. Those detected latent activity behaviors are equally present across cities and cannot be fully explained by main demographic features. Finally, we find those latent behaviors are associated with dynamics like experienced income segregation, transportation, or healthy behaviors in cities, even after controlling for demographic features. Our results signal the importance of complementing traditional census data with activity behaviors to understand urban dynamics.

Supplementary Information

The online version contains supplementary material available at 10.1140/epjds/s13688-023-00390-w.

Social networks are shaped by culturally contingent assessments of social competence

Cultural outsiders, like immigrants or international students, often struggle to make friends. We propose that one barrier to social connection is not knowing what it means to be socially competent in the host culture. First-year students at a U.S. business school (N = 1328) completed a social network survey and rated their own social competence and that of several peers. International students were rated by peers as less socially competent than U.S. students, especially if they were from nations more culturally dissimilar to the U.S. International students' self-reported competence ratings were uncorrelated with peers' judgments. Social network analysis revealed international students were less central to their peer networks than U.S. students, although this gap was reduced if peers evaluated them as socially competent. Peer-reported competence mediated the effects of international student status on social network centrality. Since learning local norms takes time, we suggest inclusivity will require host communities to define social competence more broadly.

The role of the big geographic sort in online news circulation among U.S. Reddit users

Past research has attributed the circulation of online news to two main factors-individual characteristics (e.g., a person's information literacy) and social media effects (e.g., algorithm-mediated information diffusion)-and has overlooked a third one: the critical mass created by the offline self-segregation of Americans into like-minded geographical regions such as states (a phenomenon called 'The Big Sort'). We hypothesized that this latter factor matters for the online spreading of news not least because online interactions, despite having the potential of being global, end up being localized: interaction probability is known to rapidly decay with distance. Upon analysis of more than 8M Reddit comments containing news links spanning four years, from January 2016 to December 2019, we found that Reddit did not work as an 'hype machine' for news (as opposed to what previous work reported for other platforms, circulation was not mainly caused by platform-facilitated network effects). Rather, news circulation in Reddit worked as a supply-and-demand system: news items scaled linearly with the number of users in each state (with a scaling exponent [Formula: see text]  [Formula: see text], and a goodness of fit [Formula: see text]). Furthermore, deviations from such a universal pattern were best explained by state-level personality and cultural factors ([Formula: see text]), rather than socioeconomic conditions ([Formula: see text]) or political characteristics ([Formula: see text]). Higher-than-expected circulation of any type of news was found in states characterised by residents who tend to be less diligent in terms of their personality (low in conscientiousness) and by loose cultures understating the importance of adherence to norms (low in cultural tightness). Interestingly, the combination of those factors with low levels of education was then associated with the circulation of a particular type of news, that is, misinformation. These results suggest that online interactions are geographically bounded and, as such, news circulation cannot be studied purely as an Internet phenomenon but should be grounded into a user's offline cultural environment, which has become increasingly segregated over the decades, and is admittedly hard to change.

Behavioral changes during the COVID-19 pandemic decreased income diversity of urban encounters

Diversity of physical encounters in urban environments is known to spur economic productivity while also fostering social capital. However, mobility restrictions during the pandemic have forced people to reduce urban encounters, raising questions about the social implications of behavioral changes. In this paper, we study how individual income diversity of urban encounters changed during the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in Boston, Dallas, Los Angeles, and Seattle, across three years spanning before and during the pandemic. We find that the diversity of urban encounters has substantially decreased (by 15% to 30%) during the pandemic and has persisted through late 2021, even though aggregated mobility metrics have recovered to pre-pandemic levels. Counterfactual analyses show that behavioral changes including lower willingness to explore new places further decreased the diversity of encounters in the long term. Our findings provide implications for managing the trade-off between the stringency of COVID-19 policies and the diversity of urban encounters as we move beyond the pandemic.

The Relationship between Neighborhood Social Capital and the Health of Chinese Urban Elderly: An Analysis Based on CHARLS2018 Data

There is growing interest in the relationship between neighborhood social capital and the health of urban older people, but existing research still falls short in exploring the relationship between the two. Based on 2018 CHARLS data, this paper quantitatively examines the association between neighborhood social capital and the self-rated health of urban older people. The study found that, after controlling for a series of variables, both increased social interaction and increased frequency of social interaction significantly improved urban older people's self-rated health. To implement the Health China strategy and improve the health of urban older people, further attention should be paid to the role of neighborhood social capital, creating a harmonious environment for neighborhood interaction and promoting the cultivation of neighborhood social capital.

Spatiotemporal Trends in Self-Reported Mask-Wearing Behavior in the United States: Analysis of a Large Cross-sectional Survey

BACKGROUND

Face mask wearing has been identified as an effective strategy to prevent the transmission of SARS-CoV-2, yet mask mandates were never imposed nationally in the United States. This decision resulted in a patchwork of local policies and varying compliance, potentially generating heterogeneities in the local trajectories of COVID-19 in the United States. Although numerous studies have investigated the patterns and predictors of masking behavior nationally, most suffer from survey biases and none have been able to characterize mask wearing at fine spatial scales across the United States through different phases of the pandemic.

OBJECTIVE

Urgently needed is a debiased spatiotemporal characterization of mask-wearing behavior in the United States. This information is critical to further assess the effectiveness of masking, evaluate the drivers of transmission at different time points during the pandemic, and guide future public health decisions through, for example, forecasting disease surges.

METHODS

We analyzed spatiotemporal masking patterns in over 8 million behavioral survey responses from across the United States, starting in September 2020 through May 2021. We adjusted for sample size and representation using binomial regression models and survey raking, respectively, to produce county-level monthly estimates of masking behavior. We additionally debiased self-reported masking estimates using bias measures derived by comparing vaccination data from the same survey to official records at the county level. Lastly, we evaluated whether individuals' perceptions of their social environment can serve as a less biased form of behavioral surveillance than self-reported data.

RESULTS

We found that county-level masking behavior was spatially heterogeneous along an urban-rural gradient, with mask wearing peaking in winter 2021 and declining sharply through May 2021. Our results identified regions where targeted public health efforts could have been most effective and suggest that individuals' frequency of mask wearing may be influenced by national guidance and disease prevalence. We validated our bias correction approach by comparing debiased self-reported mask-wearing estimates with community-reported estimates, after addressing issues of a small sample size and representation. Self-reported behavior estimates were especially prone to social desirability and nonresponse biases, and our findings demonstrated that these biases can be reduced if individuals are asked to report on community rather than self behaviors.

CONCLUSIONS

Our work highlights the importance of characterizing public health behaviors at fine spatiotemporal scales to capture heterogeneities that may drive outbreak trajectories. Our findings also emphasize the need for a standardized approach to incorporating behavioral big data into public health response efforts. Even large surveys are prone to bias; thus, we advocate for a social sensing approach to behavioral surveillance to enable more accurate estimates of health behaviors. Finally, we invite the public health and behavioral research communities to use our publicly available estimates to consider how bias-corrected behavioral estimates may improve our understanding of protective behaviors during crises and their impact on disease dynamics.

Reducing Health Disparities in Asthma: How Can Progress Be Made

Health disparities (recently defined as a health difference closely linked with social, economic, and/or environmental disadvantage) in asthma continue despite the presence of safe and effective treatment. For example, in the United States, Black individuals have a hospitalization rate that is 6× higher than that for White individuals, and an asthma mortality rate nearly 3× higher. This article will discuss the current state of health disparities in asthma in the United States. Factors involved in the creation of these disparities (including unconscious bias and structural racism) will be examined. The types of asthma interventions (including case workers, technological advances, mobile asthma clinics, and environmental remediation) that have and have not been successful to decrease disparities will be reviewed. Finally, current resources and future actions are summarized in a table and in text, providing information that the allergist can use to make an impact on asthma health disparities in 2023.

Measuring disadvantage: A systematic comparison of United States small-area disadvantage indices

Extensive evidence demonstrates the effects of area-based disadvantage on a variety of life outcomes, such as increased mortality and low economic mobility. Despite these well-established patterns, disadvantage, often measured using composite indices, is inconsistently operationalized across studies. To address this issue, we systematically compared 5 U.S. disadvantage indices at the county-level on their relationships to 24 diverse life outcomes related to mortality, physical health, mental health, subjective well-being, and social capital from heterogeneous data sources. We further examined which domains of disadvantage are most important when creating these indices. Of the five indices examined, the Area Deprivation Index (ADI) and Child Opportunity Index 2.0 (COI) were most related to a diverse set of life outcomes, particularly physical health. Within each index, variables from the domains of education and employment were most important in relationships with life outcomes. Disadvantage indices are being used in real-world policy and resource allocation decisions; an index's generalizability across diverse life outcomes, and the domains of disadvantage which constitute the index, should be considered when guiding such decisions.

Heterogenous effects of the Great East Japan earthquake on prosociality of people depending on their age

This study investigates the instability of prosociality in the real world by looking at the age-specific non-linear relationship between disaster exposure and prosocial behavior. We employed unique microdata from two communities in Japan that were hit by the Great East Japan Earthquake and Tsunami disaster in 2011. Exploiting exogenous variations in disaster exposure, we find age-specific heterogeneous effects of disaster exposure on prosocial behavior captured by the behavior of sending New Year's cards as well as attitudinal survey questions. Among the older groups, disaster damages undermine prosociality, whereas the younger groups show reinforced prosocial behaviors. These findings can be explained consistently by combining two possible determinants of prosocial behavior: pure or impure altruism and self-enforcements in repeated interactions at workplaces. Age information can help disentangle these two elements at least partially.

Weak ties, failed tries, and success

A large-scale study provides a causal test for a cornerstone of social science

Social capital II: determinants of economic connectedness

Low levels of social interaction across class lines have generated widespread concern^1–4 and are associated with worse outcomes, such as lower rates of upward income mobility^4–7. Here we analyse the determinants of cross-class interaction using data from Facebook, building on the analysis in our companion paper⁷. We show that about half of the social disconnection across socioeconomic lines—measured as the difference in the share of high-socioeconomic status (SES) friends between people with low and high SES—is explained by differences in exposure to people with high SES in groups such as schools and religious organizations. The other half is explained by friending bias—the tendency for people with low SES to befriend people with high SES at lower rates even conditional on exposure. Friending bias is shaped by the structure of the groups in which people interact. For example, friending bias is higher in larger and more diverse groups and lower in religious organizations than in schools and workplaces. Distinguishing exposure from friending bias is helpful for identifying interventions to increase cross-SES friendships (economic connectedness). Using fluctuations in the share of students with high SES across high school cohorts, we show that increases in high-SES exposure lead low-SES people to form more friendships with high-SES people in schools that exhibit low levels of friending bias. Thus, socioeconomic integration can increase economic connectedness in communities in which friending bias is low. By contrast, when friending bias is high, increasing cross-SES interactions among existing members may be necessary to increase economic connectedness. To support such efforts, we release privacy-protected statistics on economic connectedness, exposure and friending bias for each ZIP (postal) code, high school and college in the United States at https://www.socialcapital.org.

Social disconnection across socioeconomic lines is explained by both differences in exposure to people with high socioeconomic status and friending bias—the tendency for people to befriend peers with similar socioeconomic status even conditional on exposure.