Socio-spatial health disparities in Covid-19 cases and deaths in United States skilled nursing facilities over 30 months.

BACKGROUND: This study investigated whether socio-spatial factors surrounding United States skilled nursing facilities related to Covid-19 case counts among residents, staff, and facility personnel and deaths among residents. METHODS: With data on 12,403 United States skilled nursing facilities and Census data we estimated multilevel models to assess relationships between facility and surrounding area characteristics from June 2020 to September 2022 for cumulative resident and facility personnel case counts and resident deaths. RESULTS: Facilities with more Black or Latino residents experienced more cases incident rate ratios (IRR = 1.005; 1.004) and deaths (IRR = 1.008) among residents during the first 6 months of the pandemic but were no different thereafter. Facilities with more racial and ethnic heterogeneity and percent Black or Latino in the surrounding buffer experienced more Covid-19 cases and deaths in the first 6 months, but no such differences were observed in the subsequent 24 months. Facilities surrounded by higher percent Latino consistently experienced more cases among staff and facility personnel over the study period (IRR = 1.006; 1.001). CONCLUSIONS: Findings indicated socio-spatial health disparities in cases among residents, staff, and facility personnel in the first 6 months of the pandemic, with some disparities fading thereafter. This pattern likely suggests the importance of the adoption and adherence to pandemic-related safety measures in skilled nursing facilities nationwide.

Geographical patterns of social cohesion drive disparities in early COVID infection hazard.

The uneven spread of COVID-19 has resulted in disparate experiences for marginalized populations in urban centers. Using computational models, we examine the effects of local cohesion on COVID-19 spread in social contact networks for the city of San Francisco, finding that more early COVID-19 infections occur in areas with strong local cohesion. This spatially correlated process tends to affect Black and Hispanic communities more than their non-Hispanic White counterparts. Local social cohesion thus acts as a potential source of hidden risk for COVID-19 infection.

Insight into Selecting Adolescents for Drinking Intervention Programs: a Simulation Based on Stochastic Actor-Oriented Models.

Adolescent drinking remains a prominent public health and socioeconomic issue in the USA with costly consequences. While numerous drinking intervention programs have been developed, there is little guidance whether certain strategies of participant recruitment are more effective than others. The current study aims at addressing this gap in the literature using a computer simulation approach, a more cost-effective method than employing actual interventions. We first estimate stochastic actor-oriented models for two schools from the National Longitudinal Study of Adolescent to Adult Health (Add Health). We then employ different strategies for selecting adolescents for the intervention (either based on their drinking levels or their positions in the school network) and simulate the estimated model forward in time to assess the aggregated level of drinking in the school at a later time point. The results suggest that selecting moderate or heavy drinkers for the intervention produces better results compared to selecting casual or light drinkers. The intervention results are improved further if network position information is taken into account, as selecting drinking adolescents with higher in-degree or higher eigenvector centrality values for intervention yields the best results. Results from this study help elucidate participant selection criteria and targeted network intervention strategies for drinking intervention programs in the USA.

The Moderating Role of Context: Relationships between Individual Behaviors and Social Networks.

A social context can be viewed as an entity or unit around which a group of individuals organize their activities and interactions. Social contexts take such diverse forms as families, dwelling places, neighborhoods, classrooms, schools, workplaces, voluntary organizations, and sociocultural events or milieus. Understanding social contexts is essential for the study of individual behaviors, social networks, and the relationships between the two. Contexts shape individual behaviors by providing an avenue for non-dyadic conformity and socialization processes. The co-participation within a context affects personal relationships by acting as a focus for tie formation. Where participation in particular contexts confers status, this effect may also lead to differences in popularity within interpersonal networks. Social contexts may further play a moderating role in within-network influence and selection processes, providing circumstances that either amplify or suppress these effects. In this paper we investigate the joint role of co-participation via social contexts and dyadic interaction in shaping and being shaped by individual behaviors with the context of a U.S. high school. Implications for future study of social contexts are suggested.

A multi-contextual examination of non-school friendships and their impact on adolescent deviance and alcohol use.

Despite decades of research on adolescent friendships, little is known about adolescents who are more likely to form ties outside of school. We examine multiple social and ecological contexts including parents, the school, social networks, and the neighborhood to understand the origins and health significance of out of school ties using survey data from the National Longitudinal Study of Adolescent to Adult Health (N = 81,674). Findings indicate that out of school (more than in-school) friendships drive adolescent deviance and alcohol use, and youth with such friends tend to be involved in school activities and are central among their peer group. This suggests that intervention efforts aimed at reducing deviance and underage drinking may benefit from engaging youth with spanning social ties.

Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity.

Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible-infectious-recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 US cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly nonuniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform health care planning, predict community outcomes, or identify potential disparities.

Neighborhood change from the bottom Up: What are the determinants of social distance between new and prior residents?

An important source of neighborhood change occurs when there is a turnover in the housing unit due to residential mobility and the new residents differ from the prior residents based on socio-demographic characteristics (what we term social distance). Nonetheless, research has typically not asked which characteristics explain transitions with higher social distance based on a number of demographic dimensions. We explore this question using American Housing Survey data from 1985 to 2007, and focus on instances in which the prior household moved out and is replaced by a new household. We focus on four key characteristics for explaining this social distance: the type of housing unit, the age of the housing unit, the length of residence of the exiting household, and the crime and social disorder in the neighborhood. We find that transitions in the oldest housing units and for the longest tenured residents result in the greatest amount of social distance between new and prior residents, implying that these transitions are particularly important for fostering neighborhood socio-demographic change. The results imply micro-mechanisms at the household level that might help explain net change at the neighborhood level.

Fight or flight? Crime as a driving force in business failure and business mobility.

A growing body of research has documented the consequences of neighborhood crime for a myriad of individual, household, and community outcomes. Given that neighborhood businesses figure into the link between neighborhood structure and crime as sources of employment or sites for neighbor interaction, the present study examines the extent to which neighborhood crime is associated with the survival, mobility, and destination locations of businesses in the subsequent year. Using business data from Reference USA (Infogroup, 2015) and crime data from the Southern California Crime Study (SCCS) we assess this question for neighborhoods across cities in the Southern California region. We find that in general, higher violent and property crime are significantly associated with both business failure and mobility, and that higher crime in a destination neighborhood reduces the likelihood that a business locates there. We also present findings specific to industries, and discuss the implications of our findings for future research.

How great and how good?: Third places, neighbor interaction, and cohesion in the neighborhood context.

Though Ray Oldenburg's (1989) notion of "third places", or places conducive to sociality outside of the realms of home and work, has received both scholarly and popular attention over the past several decades, many of the author's central claims remain empirically untested. The present study considers the association between neighborhood third places, cohesion and neighbor interaction. Drawing on various literatures regarding interaction in public space and neighborhood use-value, we consider how the role of third places might vary according to neighborhood socioeconomic context. Using data from Wave I of the Los Angeles Family and Neighborhood Study (LAFANS) and data on third places from the point-based business data of ReferenceUSA, we test the effect of third places on cohesion and neighbor interaction across neighborhood poverty strata. We find support for the hypothesis that third places are associated with greater cohesion and neighbor interaction, and that neighbor interaction mediates the relationship between third places and cohesion in poor neighborhoods.

Parks as crime inhibitors or generators: Examining parks and the role of their nearby context.

Although neighborhood studies often focus on the presence of some particular entity and its consequences for a variety of local processes, a frequent limitation is the failure to account more broadly for the local context. This paper therefore examines the role of parks for community crime, but contributes to the literature by testing whether the context of land uses and demographics nearby parks moderate the parks and crime relationship. A key feature of our approach is that we also test how these characteristics explain crime in the park, nearby the park, and in other neighborhoods in the city with data from nine cities across the United States (N = 109,808 blocks). We use multilevel Poisson and negative binomial regressions to test our ideas for six types of street crime. Our findings show that nearby land uses and socio-demographic characteristics are a key driver of crime being located within the park or nearby the park. Our results also show a clear distance decay pattern for the impact of various land uses and socio-demographics nearby parks. The results emphasize a need for research to consider the broader socio-spatial context in which crime generators/inhibitors are embedded.

Growing pains or appreciable gains? Latent classes of neighborhood change, and consequences for crime in Southern California neighborhoods.

This study explored the dynamic nature of neighborhoods using a relatively novel approach and data source. By using a nonparametric holistic approach of neighborhood change based on latent class analysis (LCA), we have explored how changes in the socio-demographic characteristics of residents, as well as home improvement and refinance activity by residents, are related to changes in neighborhood crime over a decade. Utilizing annual home mortgage loan data in the city of Los Angeles from the years 2000-2010, we 1) conducted principle components factor analyses using measures of residential in-migration and home investment activities; 2) estimated LCA models to identify classes of neighborhoods that shared common patterns of change over the decade; 3) described these 11 classes; 4) estimated change-score regression models to assess the relationship of these classes with changing crime rates. The analyses detected six broad types of neighborhood change: 1) stability; 2) urban investors; 3) higher-income home buyers; 4) in-mover oscillating; 5) oscillating refinance; 6) mixed-trait. The study describes the characteristics of each of these classes, and how they are related to changes in crime rates over the decade.

The interdependence of cigarette, alcohol, and marijuana use in the context of school-based social networks.

The concurrent or sequential usage of multiple substances during adolescence is a serious public health problem. Given the importance of understanding interdependence in substance use during adolescence, the purpose of this study is to examine the co-evolution of cigarette smoking, alcohol, and marijuana use within the ever-changing landscape of adolescent friendship networks, which are a primary socialization context for adolescent substance use. Utilizing Stochastic Actor-Based models, we examine how multiple simultaneous social processes co-evolve with adolescent smoking, drinking, and marijuana use within adolescent friendship networks using two school samples from early waves of the National Longitudinal Study of Adolescent to Adult Health (Add Health). We also estimate two separate models examining the effects from using one substance to the initiation and cessation of other substances for each sample. Based on the initial model results, we simulate the model forward in time by turning off one key effect in the estimated model at a time, and observe how the distribution of use of each substance changes. We find evidence of a unilateral causal relationship from marijuana use to subsequent smoking and drinking behaviors, resulting in the initiation of drinking behavior. Marijuana use is also associated with smoking initiation in a school with a low substance use level, and smoking cessation in a school with a high substance use level. In addition, in a simulation model excluding the effect from marijuana use to smoking and drinking behavior, the number of smokers and drinkers decreases precipitously. Overall, our findings indicate some evidence of sequential drug use, as marijuana use increased subsequent smoking and drinking behavior and indicate that an adolescent's level of marijuana use affects the initiation and continuation of smoking and drinking.

Problems, perceptions and actions: An interdependent process for generating informal social control.

Using two waves of survey data for residents in neighborhoods in Brisbane, this study explores the interdependent relationship between residents' perceptions of neighboring, cohesion, collective efficacy, neighborhood disorder, and the actions they take to address these problems. Our longitudinal results show that residents' perceived severity of a problem helps explain engaging in activity to address the problem. People loitering appeared to be the most galvanizing problem for residents, but had particularly deleterious effects on perceptions of cohesion and collective efficacy. We also find that residents who perceive more neighboring in their local area engage in more public and parochial social control activity and residents who live in collectively efficacious neighborhoods are more likely to engage in parochial social control action. Furthermore, residents who themselves perceive more collective efficacy in the neighborhood engage in more parochial or public social control during the subsequent time period. Importantly, we find strong evidence that residents update their sense of collective efficacy. Perceiving more problems in the neighborhood, and perceiving that these problems are increasing, reduced perceptions of neighboring and collective efficacy over time.

Perceptions as the crucial link? The mediating role of neighborhood perceptions in the relationship between the neighborhood context and neighborhood cohesion.

This study examines the effects of neighborhood racial in-group size, economic deprivation and the prevalence of crime on neighborhood cohesion among U.S. whites. We explore to what extent residents' perceptions of their neighborhood mediate these macro-micro relationships. We use a recent individual-level data set, the American Social Fabric Study (2012/2013), enriched with contextual-level data from the U.S. Census Bureau (2010) and employ multi-level structural equation models. We show that the racial in-group size is positively related to neighborhood cohesion and that neighborhood cohesion is lower in communities with a high crime rate. Individuals' perceptions of the racial in-group size partly mediate the relationship between the objective racial in-group size and neighborhood cohesion. Residents' perceptions of unsafety from crime also appear to be a mediating factor, not only for the objective crime rate but also for the objective racial in-group size. This is in line with our idea that racial stereotypes link racial minorities to crime whereby neighborhoods with a large non-white population are perceived to be more unsafe. Residents of the same neighborhood differ in how they perceive the degree of economic decay of the neighborhood and this causes them to evaluate neighborhood cohesion differently, however perceptions of neighborhood economic decay do not explain the link between the objective neighborhood context and neighborhood cohesion.

Studying neighborhood crime across different macro spatial scales: The case of robbery in 4 cities.

Whereas there is a burgeoning literature focusing on the spatial distribution of crime events across neighborhoods or micro-geographic units in a specific city, the present study expands this line of research by selecting four cities that vary across two macro-spatial dimensions: population in the micro-environment, and population in the broader macro-environment. We assess the relationship between measures constructed at different spatial scales and robbery rates in blocks in four cities: 1) San Francisco (high in micro- and macro-environment population); 2) Honolulu (high in micro- but low in macro-environment population); 3) Los Angeles (low in micro- but high in macro-environment population); 4) Sacramento (low in micro- and macro-environment population). Whereas the socio-demographic characteristics of residents further than ½ mile away do not impact robbery rates, the number of people up to 2.5 miles away are related to robbery rates, especially in the two cities with smaller micro-environment population, implying a larger spatial scale than is often considered. The results show that coefficient estimates differ somewhat more between cities differing in micro-environment population compared to those differing based on macro-environment population. It is therefore necessary to consider the broader macro-environment even when focusing on the level of crime across neighborhoods or micro-geographic units within an area.

Cascades of emotional support in friendship networks and adolescent smoking.

Social support from peers and parents provides a key socialization function during adolescence. We examine adolescent friendship networks using a Stochastic Actor-Based modeling approach to observe the flow of emotional support provision to peers and the effect of support from parents, while simultaneously modeling smoking behavior. We utilized one school (n = 976) from The National Longitudinal Study of Adolescent to Adult Health (AddHealth) Study. Our findings suggest that emotional support is transacted through an interdependent contextual system, comprised of both peer and parental effects, with the latter also having distal indirect effects from youths' friends' parents.

Peer Influence, Peer Selection and Adolescent Alcohol Use: a Simulation Study Using a Dynamic Network Model of Friendship Ties and Alcohol Use.

While studies suggest that peer influence can in some cases encourage adolescent substance use, recent work demonstrates that peer influence may be on average protective for cigarette smoking, raising questions about whether this effect occurs for other substance use behaviors. Herein, we focus on adolescent drinking, which may follow different social dynamics than smoking. We use a data-calibrated Stochastic Actor-Based (SAB) Model of adolescent friendship tie choice and drinking behavior to explore the impact of manipulating the size of peer influence and selection effects on drinking in two school-based networks. We first fit a SAB Model to data on friendship tie choice and adolescent drinking behavior within two large schools (n = 2178 and n = 976) over three time points using data from the National Longitudinal Study of Adolescent to Adult Health. We then alter the size of the peer influence and selection parameters with all other effects fixed at their estimated values and simulate the social systems forward 1000 times under varying conditions. Whereas peer selection appears to contribute to drinking behavior similarity among adolescents, there is no evidence that it leads to higher levels of drinking at the school level. A stronger peer influence effect lowers the overall level of drinking in both schools. There are many similarities in the patterning of findings between this study of drinking and previous work on smoking, suggesting that peer influence and selection may function similarly with respect to these substances.

Collective efficacy: How is it conceptualized, how is it measured, and does it really matter for understanding perceived neighborhood crime and disorder?

Building on the insights of the self-efficacy literature, this study highlights that collective efficacy is a collective perception that comes from a process. This study emphasizes that 1) there is updating, as there are feedback effects from success or failure by the group to the perception of collective efficacy, and 2) this updating raises the importance of accounting for members' degree of uncertainty regarding neighborhood collective efficacy. Using a sample of 113 block groups in three rural North Carolina counties, this study finds evidence of updating as neighborhoods perceiving more crime or disorder reported less collective efficacy at the next time point. Furthermore, collective efficacy was only associated with lower perceived disorder at the next time point when it occurred in highly cohesive neighborhoods. Finally, neighborhoods with more perceived disorder and uncertainty regarding collective efficacy at one time point had lower levels of collective efficacy at the next time point, illustrating the importance of uncertainty along with updating.

Coevolution of adolescent friendship networks and smoking and drinking behaviors with consideration of parental influence.

Friendship tie choices in adolescent social networks coevolve simultaneously with youths' cigarette smoking and drinking. We estimate direct and multiplicative relationships between both peer influence and peer selection with salient parental factors affecting both friendship tie choice and the use of these 2 substances. We utilize 1 sample of 12 small schools and a single large school extracted from the National Longitudinal Study of Adolescent to Adult Health. Using a Stochastic Actor-Based modeling approach over 3 waves, we find: (a) a peer selection effect, as adolescents nominated others as friends based on cigarette and alcohol use levels across samples; (b) a peer influence effect, as adolescents adapted their smoking and drinking behaviors to those of their best friends across samples; (c) reciprocal effect between cigarette and alcohol usage in the small school sample; (d) a direct effect of parental support and the home smoking environment on adolescent friendship tie choice in the small school sample; (e) a direct effect of the home smoking environment on smoking across samples; (f) a direct effect of the home drinking environment on alcohol use across samples; and (g) a direct effect of parental monitoring on alcohol use across samples. We observed an interaction between parental support and peer influence in affecting drinking, and an interaction between the home drinking environment and peer influence on drinking, in the small school sample. Our findings suggested the importance of delineating direct and synergistic pathways linking network processes and parental influence as they affect concurrent cigarette and alcohol use. (PsycINFO Database Record

Multiple Imputation for Missing Edge Data: A Predictive Evaluation Method with Application to Add Health.

Recent developments have made model-based imputation of network data feasible in principle, but the extant literature provides few practical examples of its use. In this paper we consider 14 schools from the widely used In-School Survey of Add Health (Harris et al., 2009), applying an ERGM-based estimation and simulation approach to impute the network missing data for each school. Add Health's complex study design leads to multiple types of missingness, and we introduce practical techniques for handing each. We also develop a cross-validation based method - Held-Out Predictive Evaluation (HOPE) - for assessing this approach. Our results suggest that ERGM-based imputation of edge variables is a viable approach to the analysis of complex studies such as Add Health, provided that care is used in understanding and accounting for the study design.