Social capital II: determinants of economic connectedness

Exploring the mechanisms underlying increased risk of depressive disorder in ethnic minority populations in Europe: A causal loop diagram

BACKGROUND

Multiple ethnic minority populations in Europe show high risk of major depressive disorder (MDD), with ethnic discrimination and low socioeconomic position (SEP) as established risk factors. How this risk is shaped by the interactions between these, and other social factors, remains to be elucidated. We aimed to develop a causal-loop diagram (CLD) to gain a better understanding of how factors at the intersection of ethnic discrimination and SEP dynamically interact to drive MDD risk.

METHODS

We iteratively mapped the interactions and feedback loops between factors at the intersection of ethnic discrimination and SEP, drawing input from (i) a series of two interviews with a range of MDD domain experts, (ii) an existing CLD mapping the onset of MDD across psychological, biological, and social dimensions at the level of the individual, and (iii) other relevant literature.

RESULTS

Through tracing the feedback loops in the resulting CLD, we identified ten driving mechanisms for MDD onset in ethnic minorities (two related to ethnic discrimination, SEP, social network and support, and acculturation, as well as one relating to the living environment and self-stigma towards MDD); and four factors that modulate these mechanisms (recent migration, religious affiliation, neighborhood social environment, and public stigma towards MDD). The intersecting nature of ethnic discrimination and SEP, combined with the reinforcing dynamics of the identified driving mechanisms across time- and spatial scales, underscores the excess exposure to circumstances that increase MDD risk in ethnic minorities.

CONCLUSIONS

While this CLD requires validation through future studies, the intersecting and reinforcing nature of the identified driving mechanisms highlights that tackling the high risk of MDD in ethnic minorities may require intervening at multiple targets, from the individual (e.g., psychological interventions targeting negative beliefs or reducing stress) to the societal level (e.g., addressing labor market discrimination).

Social microclimates and well-being

Emotional well-being has a known relationship with a person's direct social ties, including friendships; but do ambient social and emotional features of the local community also play a role? This work takes advantage of university students' assignment to different local networks-or "social microclimates"-to probe this question. Using Least Absolute Shrinkage and Selection Operator (LASSO) regression, we quantify the collective impact of individual, social network, and microclimate factors on the emotional well-being of a cohort of first-year college students. Results indicate that well-being tracks individual factors but also myriad social and microclimate factors, reflecting one's peers and social surroundings. Students who belonged to emotionally stable and tight-knit microclimates (i.e., had emotionally stable friends or resided in densely connected residence halls) reported lower levels of psychological distress and higher levels of life satisfaction, even when controlling for factors such as personality and social network size. Although rarely discussed or acknowledged in the policies that create them, social microclimates are consequential to well-being, especially during life transitions. The effects of microclimate factors are small relative to some individual factors; however, they explain unique variance in well-being that is not directly captured by emotional stability or other individual factors. These findings are novel, but preliminary, and should be replicated in new samples and contexts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

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.

Childhood neighborhoods and cause-specific adult mortality in Sweden 1939-2015

The socioeconomic health gradient has widened in recent decades. We study how childhood socioeconomic neighborhood conditions influence gender- and cause-specific adult mortality. Using uniquely detailed geocoded longitudinal microdata for a Swedish town (1939-1967), with a follow-up in national registers (1968-2015), we apply Cox proportional hazards models and estimate individual neighborhoods at the address-level. We find that childhood neighborhood social class has a lasting influence on male adult mortality (ages 40-69), even when adjusting for class position, class origin, neighborhood physical attributes and school districts. This impact was particularly pronounced for preventable causes of death, pointing to lifestyle and behavioral factors as important mechanisms.

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.

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.

Association of Population Well-Being With Cardiovascular Outcomes

Importance

Mortality from cardiovascular disease (CVD) varies across communities and is associated with known structural and population health factors. Still, a population's well-being, including sense of purpose, social relationships, financial security, and relationship to community, may be an important target to improve cardiovascular health.

Objective

To examine the association of population level measures of well-being with rates of CVD mortality in the US.

Design, Setting, and Participants

This cross-sectional study linked data from the Gallup National Health and Well-Being Index (WBI) survey to county-level rates of CVD mortality from the Centers for Disease Control and Prevention Atlas of Heart Disease and Stroke. Participants were respondents of the WBI survey, which was conducted by Gallup with randomly selected adults aged 18 years or older from 2015 to 2017. Data were analyzed from August 2022 to May 2023.

Main Outcomes and Measures

The primary outcome was the county-level rate of total CVD mortality; secondary outcomes were mortality rates for stroke, heart failure, coronary heart disease, acute myocardial infarction, and total heart disease. The association of population well-being (measured using a modified version of the WBI) with CVD mortality was assessed, and an analysis of whether the association was modified by county structural factors (Area Deprivation Index [ADI], income inequality, and urbanicity) and population health factors (percentages of the adult population who had hypertension, diabetes, or obesity; were currently smoking; and were physically inactive) was conducted. Population WBI and its ability to mediate the association of structural factors associated with CVD using structural equation models was also assessed.

Results

Well-being surveys were completed by 514 971 individuals (mean [SD] age 54.0 [19.2] years; 251 691 [48.9%] women; 379 521 [76.0%] White respondents) living in 3228 counties. Mortality rates for CVD decreased from a mean of 499.7 (range, 174.2-974.7) deaths per 100 000 persons in counties with the lowest quintile of population well-being to 438.6 (range, 110.1-850.4) deaths per 100 000 persons in counties with the highest quintile of population well-being. Secondary outcomes showed similar patterns. In the unadjusted model, the effect size (SE) of WBI on CVD mortality was -15.5 (1.5; P < .001), or a decrease of 15 deaths per 100 000 persons for each 1-point increase of population well-being. After adjusting for structural factors and structural plus population health factors, the association was attenuated but still significant, with an effect size (SE) of -7.3 (1.6; P < .001); for each 1-point increase in well-being, the total cardiovascular death rate decreased by 7.3 deaths per 100 000 persons. Secondary outcomes showed similar patterns, with mortality due to coronary heart disease and heart failure being significant in fully adjusted models. In mediation analyses, associations of income inequality and ADI with CVD mortality were all partly mediated by the modified population WBI.

Conclusions and Relevance

In this cross-sectional study assessing the association of well-being and cardiovascular outcomes, higher well-being, a measurable, modifiable, and meaningful outcome, was associated with lower CVD mortality, even after controlling for structural and cardiovascular-related population health factors, indicating that well-being may be a focus for advancing cardiovascular health.

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.

Personal sustained cooperation based on networked evolutionary game theory

Evolutionary game theory on complex networks provides an effective theoretical tool to explain the emergence of sustained cooperative behavior. Human society has formed various organizational networks. The network structure and individual behavior take on a variety of forms. This diversity provides the basis for choice, so it is crucial for the emergence of cooperation. This article provides a dynamic algorithm for individual network evolution, and calculates the importance of different nodes in the network evolution process. In the dynamic evolution simulation, the probability of the cooperation strategy and betrayal strategy is described. In the individual interaction network, cooperative behavior will promote the continuous evolution of individual relationships and form a better aggregative interpersonal network. The interpersonal network of betrayal has been in a relatively loose state, and its continuity must rely on the participation of new nodes, but there will be certain "weak links" in the existing nodes of the network.

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.

Social capital I: measurement and associations with economic mobility

Social capital—the strength of an individual’s social network and community—has been identified as a potential determinant of outcomes ranging from education to health^1–8. However, efforts to understand what types of social capital matter for these outcomes have been hindered by a lack of social network data. Here, in the first of a pair of papers⁹, we use data on 21 billion friendships from Facebook to study social capital. We measure and analyse three types of social capital by ZIP (postal) code in the United States: (1) connectedness between different types of people, such as those with low versus high socioeconomic status (SES); (2) social cohesion, such as the extent of cliques in friendship networks; and (3) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analysing their associations with economic mobility across areas. The share of high-SES friends among individuals with low SES—which we term economic connectedness—is among the strongest predictors of upward income mobility identified to date^10,11. Other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality^12–14. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org.

Analyses of data on 21 billion friendships from Facebook in the United States reveal associations between social capital and economic mobility.