Would I Have Your Support? Family Network Features and Past Support Exchanges Associated with Anticipated Support for a Substance Problem.

Objective: Assessment of social processes underlying anticipation for recovery-related support from family in the event of a substance problem. We drew from literature on social support, substance use, and social networks to develop a path model connecting emotionally close family relationships, closeness among members in the wider family network (density), previous emotional support exchanges, and anticipated support. Subjects and Methods: We used a sample from the 2019 Nebraska Annual Social Indicators Survey (284 adults; 57% female; 94% white; 46.26% living in rural areas) and employed generalized structural equation modeling with logistic regression equations for our binary dependent variable (anticipated support). Results: Denser family networks were associated with individuals' close relations with family (b = .18, p < .001), close family relations were associated with support received by (b = .25, p < .05) and given to (b = .47, p < .001) family, and only support given to family increased the odds of anticipated support (IRR = 4.32, CI = 1.13, 16.48). Conclusions: Family-wide dynamics are important for understanding how support exchange relates to anticipated support. Prioritizing efforts to strengthen family relationships and improve the likelihood that at-risk individuals, especially in rural areas, can overcome substance problems is important.

Short Take: Collecting Data from a Vulnerable Population during the COVID-19 Pandemic.

Conducting field research with a vulnerable population is difficult under the most auspicious conditions, and these difficulties only increase during a pandemic. Here, we describe the practical challenges and ethical considerations surrounding a recent data collection effort with a high-risk population during the COVID-19 pandemic. We detail our strategies related to research design, site selection, and ethical review.

Co-use among confidants: An examination of polysubstance use and personal relationships in southeastern Nebraska.

This study examines the relationship between personal networks and polysubstance use among people who use drugs (PWUD) in a medium sized city in the Midwest. A large body of work has demonstrated that personal relationships have an ambivalent association with substance use. On the one hand, a supportive network is associated with safer drug use practices and dramatically improves the outlook for recovery. However, individuals whose personal networks are composed of co-drug use partners are more likely to engage in risky practices. We argue that this notion of "supportive" social contacts and "risky" social contacts is ultimately incomplete: risky behaviors are introduced and further developed in a social context, often with the people who provide emotional support. We argue that personal networks with more multiplex relationships (where co-drug use and confiding fuse) are harmful because they combine norms of trust and reciprocity with drug use. We use data from the Rural Health Cohort (RHC) study to test this idea. The sample consists of 120 adult PWUD in a medium sized city located in southeastern Nebraska who were recruited using respondent-driven sampling. Participants listed up to nine confidants and nine co-drug use partners, indicating any overlap between the two networks. Our results demonstrate that multiplex ties are as strongly associated with polysubstance use as simple co-drug use relationships. As the drug crisis has increasingly shifted to underserved populations outside large urban centers, this paper represents an important advance in our understanding of the current drug crisis.

Network sampling coverage III: Imputation of missing network data under different network and missing data conditions.

Missing data is a common, difficult problem for network studies. Unfortunately, there are few clear guidelines about what a researcher should do when faced with incomplete information. We take up this problem in the third paper of a three-paper series on missing network data. Here, we compare the performance of different imputation methods across a wide range of circumstances characterized in terms of measures, networks and missing data types. We consider a number of imputation methods, going from simple imputation to more complex model- based approaches. Overall, we find that listwise deletion is almost always the worst option, while choosing the best strategy can be difficult, as it depends on the type of missing data, the type of network and the measure of interest. We end the paper by offering a set of practical outputs that researchers can use to identify the best imputation choice for their particular research setting.

Patterns of Missing Data With Ecological Momentary Assessment Among People Who Use Drugs: Feasibility Study Using Pilot Study Data.

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

Practical problems and positive experiences with ecological momentary assessment: reflections from people who use drugs.

Background: Ecological momentary assessment (EMA) is an increasingly popular and feasible form of data collection, but it can be intensive and intrusive. Especially for at-risk, vulnerable populations like people who use drugs (PWUD), poor experiences with EMA may exacerbate existing chronic struggles while decreasing response rates. However, little research queries participants' experiences with EMA studies.Objectives: We explore participants' positive and negative experiences with EMA, identifying what they liked about the study, the problems they experienced, and suggested solutions to these problems.Methods: Results come from semi-structured interviews from 26 PWUD (6 women; 20 men) in Nebraska who participated in a two-week EMA pilot study on drug use with a study-provided smartphone. Participant responses were recorded by interviewers into open-text fields in Qualtrics. Data were analyzed with an iterative open coding procedure.Results: We found that many participants enjoyed the study and seamlessly incorporated the phone into their daily lives. There were a number of negative study aspects identified, however, as many participants experienced functional issues (e.g., running out of high-speed data, trouble keeping the phone charged, not able to answer questions within the two-hour timeframe) that detracted from their experience, especially if they were homeless.Conclusion: Our findings provide methodological considerations for studies with EMA components among at-risk, vulnerable populations, like PWUD. These suggestions are targeted toward the continued ethical collection of high-quality data in clinical and non-clinical settings.

Exacerbating Inequalities: Social Networks, Racial/Ethnic Disparities, and the COVID-19 Pandemic in the United States.

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

Estimating Contextual Effects from Ego Network Data.

Network concepts are often used to characterize the features of a social context. For example, past work has asked if individuals in more socially cohesive neighborhoods have better mental health outcomes. Despite the ubiquity of use, it is relatively rare for contextual studies to employ the methods of network analysis. This is the case, in part, because network data are difficult to collect, requiring information on all ties between all actors. This paper asks whether it is possible to avoid such heavy data collection while still retaining the best features of a contextual-network study. The basic idea is to apply network sampling to the problem of contextual models, where one uses sampled ego network data to infer the network features of each context, and then uses the inferred network features as second-level predictors in a hierarchical linear model. We test the validity of this idea in the case of network cohesion. Using two complete datasets as a test, we find that ego network data are sufficient to capture the relationship between cohesion and important outcomes, like attachment and deviance. The hope, going forward, is that researchers will find it easier to incorporate holistic network measures into traditional regression models.

Immunization strategies in networks with missing data.

Network-based intervention strategies can be effective and cost-efficient approaches to curtailing harmful contagions in myriad settings. As studied, these strategies are often impractical to implement, as they typically assume complete knowledge of the network structure, which is unusual in practice. In this paper, we investigate how different immunization strategies perform under realistic conditions-where the strategies are informed by partially-observed network data. Our results suggest that global immunization strategies, like degree immunization, are optimal in most cases; the exception is at very high levels of missing data, where stochastic strategies, like acquaintance immunization, begin to outstrip them in minimizing outbreaks. Stochastic strategies are more robust in some cases due to the different ways in which they can be affected by missing data. In fact, one of our proposed variants of acquaintance immunization leverages a logistically-realistic ongoing survey-intervention process as a form of targeted data-recovery to improve with increasing levels of missing data. These results support the effectiveness of targeted immunization as a general practice. They also highlight the risks of considering networks as idealized mathematical objects: overestimating the accuracy of network data and foregoing the rewards of additional inquiry.

Network Effects in Blau Space: Imputing Social Context from Survey Data.

We develop a method of imputing ego network characteristics for respondents in probability samples of individuals. This imputed network uses the homophily principle to estimate certain properties of a respondent's core discussion network in the absence of actual network data. These properties measure the potential exposure of respondents to the attitudes, values, beliefs, etc. of their (likely) network alters. We use American National Election Survey data (2016 ANES) to demonstrate that the imputed network features show substantial effects on individual level measures, such as political attitudes and beliefs. In some cases, the imputed network variable substantially reduces the effects of standard socio-demographic variables like age and education. We argue that the imputed network variable captures many of the aspects of social context that have been at the core of sociological analysis for decades.

Tobacco Regulation and Cost-Benefit Analysis: How Should We Value Foregone Consumer Surplus?

Recent tobacco regulations proposed by the Food and Drug Administration have raised a thorny question: how should the cost-benefit analysis accompanying such policies value foregone consumer surplus associated with regulation-induced reductions in smoking? In a model with rational and fully informed consumers, this question is straightforward. There is disagreement, however, about whether consumers are rational and fully informed, and the literature offers little practical guidance about what approach the FDA should use if they are not. In this paper, we outline the history of the FDA's recent attempts to regulate cigarettes and other tobacco products and how they have valued foregone consumer surplus in cost-benefit analyses. We advocate replacing the approach used in most of this literature, which first calculates health gains associated with regulation and then "offsets" them by some factor reflecting consumer surplus losses, with a more general behavioral public finance framework for welfare analysis. This framework applies standard tools of welfare analysis to consumer demand that may be "biased" (that is, not necessarily rational and fully informed) without requiring specific assumptions about the reason for the bias. This framework would require estimates of both biased and unbiased consumer demand; we sketch an agenda to help develop these in the context of smoking. The use of this framework would substantially reduce the confusion currently surrounding welfare analysis of tobacco regulation.

Approximate Functional Relationship between IRT and CTT Item Discrimination Indices: A Simulation, Validation, and Practical Extension of Lord's (1980) Formula.

Lord (1980) presented a purely conceptual equation to approximate the nonlinear functional relationship between classical test theory (CTT; aka true score theory) and item response theory (IRT) item discrimination indices. The current project proposes a modification to his equation that makes it useful in practice. The suggested modification acknowledges the more common contemporary CTT discrimination index of a corrected item-total correlation and incorporates item difficulty. We simulated slightly over 768 trillion individual item responses to uncover a best-fitting empirical function relating the IRT and CTT discrimination indices. To evaluate the effectiveness of the function, we applied it to real-world test data from 16 workforce and educational tests. Our modification results in shifted functional asymptotes, slopes, and points of inflection across item difficulties. Validation with the workforce and educational tests suggests good prediction under common assumption testing conditions (approximately normal distribution of abilities and moderate item difficulties) and greater precision than Lord's (1980) formula.

Network sampling coverage II: The effect of non-random missing data on network measurement.

Missing data is an important, but often ignored, aspect of a network study. Measurement validity is affected by missing data, but the level of bias can be difficult to gauge. Here, we describe the effect of missing data on network measurement across widely different circumstances. In Part I of this study (Smith and Moody, 2013), we explored the effect of measurement bias due to randomly missing nodes. Here, we drop the assumption that data are missing at random: what happens to estimates of key network statistics when central nodes are more/less likely to be missing? We answer this question using a wide range of empirical networks and network measures. We find that bias is worse when more central nodes are missing. With respect to network measures, Bonacich centrality is highly sensitive to the loss of central nodes, while closeness centrality is not; distance and bicomponent size are more affected than triad summary measures and behavioral homophily is more robust than degree-homophily. With respect to types of networks, larger, directed networks tend to be more robust, but the relation is weak. We end the paper with a practical application, showing how researchers can use our results (translated into a publically available java application) to gauge the bias in their own data.

Brief Report: Respondent-driven Sampling Estimators Under Real and Theoretical Recruitment Conditions of Female Sex Workers in China.

We compare the performance of multiple respondent-driven sampling estimators under different sample recruitment conditions in hidden populations of female sex workers in the midst of China's ongoing epidemic of sexually transmitted infections. We first examine empirically calibrated simulations grounded in survey data to evaluate the relative performance of each estimator under ideal sampling conditions consistent with respondent-driven sampling assumptions and under conditions that mimic observed respondent-driven sampling recruitment processes. One estimator, which incorporates respondents' reports on their network of contacts, substantially out-performs the others under all conditions. We then apply the estimators to empirical samples of female sex workers collected in two Chinese cities that include unique data on respondents' networks. These empirical results are consistent with the simulation results, suggesting that traditional respondent-driven sampling estimators overestimate the proportion of female sex workers working in low tiers of sex work and are likely to overstate the sexually transmitted infection risk profiles of these populations.

Racial differences in diabetes among Union Forces during the US Civil War.

Diabetes is the seventh leading cause of death in the United States and disproportionately affects ethnic minorities. While research examining health disparities is well-established, an historical understanding of how the disparities evolved over time may be warranted. This article examined racial differences in prevalence of diabetes and associated mortality in Blacks and Whites during the US Civil War. Data were extracted from the Medical and Surgical History of the War of Rebellion, 1861-1865, representing segregated White and Black Union Forces who served during the war. Data were collapsed by war theater (Atlantic, Central, Pacific). Results by race show that, from 1861 to 1866, the rates of Whites diagnosed with diabetes ranged overall from 0% to .11% and was distributed throughout the war theaters as: Atlantic 0.3% to .05%; Central 0.3% to .08%, and Pacific 0% to .11%. For Blacks, Atlantic ranged from .02% to .07% and Central .03% to .06%. None were reported for Pacific. Mortality was approximately .01% for both Blacks and Whites. These data suggest no racial differences in diabetes prevalence and mortality existed between Blacks and Whites during this time, implying that disparities may have evolved more recently.

New evidence on the persistence of health spending.

Surprisingly little is known about long-term spending patterns in the under-65 population. Such information could inform efforts to improve coverage and control costs. Using the MarketScan claims database, we characterize the persistence of health care spending in the privately insured, under-65 population. Over a 6-year period, 69.8% of enrollees never had annual spending in the top 10% of the distribution and the bottom 50% of spenders accounted for less than 10% of spending. Those in the top 10% in 2003 were almost as likely (34.4%) to be in the top 10% five years later as one year later (43.4%). Many comorbid conditions retained much of their predictive power even 5 years later. The persistence at both ends of the spending distribution indicates the potential for adverse selection and cream skimming and supports the use of disease management, particularly for those with the conditions that remained strong predictors of high spending throughout the follow-up period.

Network Ecology and Adolescent Social Structure.

Adolescent societies-whether arising from weak, short-term classroom friendships or from close, long-term friendships-exhibit various levels of network clustering, segregation, and hierarchy. Some are rank-ordered caste systems and others are flat, cliquish worlds. Explaining the source of such structural variation remains a challenge, however, because global network features are generally treated as the agglomeration of micro-level tie-formation mechanisms, namely balance, homophily, and dominance. How do the same micro-mechanisms generate significant variation in global network structures? To answer this question we propose and test a network ecological theory that specifies the ways features of organizational environments moderate the expression of tie-formation processes, thereby generating variability in global network structures across settings. We develop this argument using longitudinal friendship data on schools (Add Health study) and classrooms (Classroom Engagement study), and by extending exponential random graph models to the study of multiple societies over time.

Structural Effects of Network Sampling Coverage I: Nodes Missing at Random1.

Network measures assume a census of a well-bounded population. This level of coverage is rarely achieved in practice, however, and we have only limited information on the robustness of network measures to incomplete coverage. This paper examines the effect of node-level missingness on 4 classes of network measures: centrality, centralization, topology and homophily across a diverse sample of 12 empirical networks. We use a Monte Carlo simulation process to generate data with known levels of missingness and compare the resulting network scores to their known starting values. As with past studies (Borgatti et al 2006; Kossinets 2006), we find that measurement bias generally increases with more missing data. The exact rate and nature of this increase, however, varies systematically across network measures. For example, betweenness and Bonacich centralization are quite sensitive to missing data while closeness and in-degree are robust. Similarly, while the tau statistic and distance are difficult to capture with missing data, transitivity shows little bias even with very high levels of missingness. The results are also clearly dependent on the features of the network. Larger, more centralized networks are generally more robust to missing data, but this is especially true for centrality and centralization measures. More cohesive networks are robust to missing data when measuring topological features but not when measuring centralization. Overall, the results suggest that missing data may have quite large or quite small effects on network measurement, depending on the type of network and the question being posed.

Unexpected gain of function for the scaffolding protein plectin due to mislocalization in pancreatic cancer.

We recently demonstrated that plectin is a robust biomarker for pancreatic ductal adenocarcinoma (PDAC), one of the most aggressive malignancies. In normal physiology, plectin is an intracellular scaffolding protein, but we have demonstrated localization on the extracellular surface of PDAC cells. In this study, we confirmed cell surface localization. Interestingly, we found that plectin cell surface localization was attributable to its presence in exosomes secreted from PDAC cells, which is dependent on the expression of integrin ß4, a protein known to interact with cytosolic plectin. Moreover, plectin expression was necessary for efficient exosome production and was required to sustain enhanced tumor growth in immunodeficient and in immunocompetent mice. It is now clear that this PDAC biomarker plays a role in PDAC, and further understanding of plectin's contribution to PDAC could enable improved therapies.

Mechanisms for targeted delivery of nanoparticles in cancer.

With the evolution of the "omics" era, our molecular understanding of cancer has exponentially increased, leading to the development of the concept of personalized medicine. Nanoparticle technology has emerged as a way to combine cancer specific targeting with multifunctionality, such as imaging and therapy, leading to advantages over conventional small molecule based approaches. In this review, we discuss the targeting mechanisms of nanoparticles, which can be passive or active. The latter utilizes small molecules, aptamers, peptides, and antibodies as targeting moieties incorporated into the nanoparticle surface to deliver personalized therapy to patients.