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.

Assessing the Efficacy of a PhotoVoice-Informed HIV Stigma Training for Health Care Workers.

HIV stigma is a harmful social phenomenon present in United States (US)-based health care settings. This study assessed the efficacy of a participatory PhotoVoice-informed stigma reduction training program focusing on people living with HIV (PLWH) and targeting health care workers. Seventy-three (N = 73) participants were assessed at baseline (T1), within approximately a week of the training (T2), and at a 3-month follow-up (T3) regarding their HIV/AIDS knowledge, attitudes towards PLWH, and observations of enacted HIV stigma. Findings indicated that the training increased knowledge and improved attitudes (ß = 0.56, p < 0.01; ß = 0.58, p < 0.01, respectively) at T2, but these effects diminished at T3 (ß = - 0.03, p > 0.05; ß = - 0.29, p > 0.05, respectively). The training did not, however, have an impact on observations of enacted stigma at T2 (ß = 0.10, p > 0.05) or at T3 (ß = 0.02, p > 0.05). Additional participatory stigma reduction programs that involve diverse groups of health care workers, offer salient study incentives, include time-saving training methods, and comprise a variety of stigma measures, may be particularly beneficial.

Randomised controlled trial evaluation of Tweet2Quit: a social network quit-smoking intervention.

BACKGROUND: We evaluated a novel Twitter-delivered intervention for smoking cessation, Tweet2Quit, which sends daily, automated communications to small, private, self-help groups to encourage high-quality, online, peer-to-peer discussions. DESIGN: A 2-group randomised controlled trial assessed the net benefit of adding a Tweet2Quit support group to a usual care control condition of nicotine patches and a cessation website. PARTICIPANTS: Participants were 160 smokers (4 cohorts of 40/cohort), aged 18-59 years, who intended to quit smoking, used Facebook daily, texted weekly, and had mobile phones with unlimited texting. INTERVENTION: All participants received 56 days of nicotine patches, emails with links to the smokefree.gov cessation website, and instructions to set a quit date within 7 days. Additionally, Tweet2Quit participants were enrolled in 20-person, 100-day Twitter groups, and received daily discussion topics via Twitter, and daily engagement feedback via text. MEASURES: The primary outcome was sustained abstinence at 7, 30 and 60 days post-quit date. RESULTS: Participants (mean age 35.7 years, 26.3% male, 31.2% college degree, 88.7% Caucasian) averaged 18.0 (SD=8.2) cigarettes per day and 16.8 (SD=9.8) years of smoking. Participants randomised to Tweet2Quit averaged 58.8 tweets/participant and the average tweeting duration was 47.4 days/participant. Tweet2Quit doubled sustained abstinence out to 60 days follow-up (40.0%, 26/65) versus control (20.0%, 14/70), OR=2.67, CI 1.19 to 5.99, p=0.017. Tweeting via phone predicted tweet volume, and tweet volume predicted sustained abstinence (p<0.001). The daily autocommunications caused tweeting spikes accounting for 24.0% of tweets. CONCLUSIONS: Tweet2Quit was engaging and doubled sustained abstinence. Its low cost and scalability makes it viable as a global cessation treatment. TRIAL REGISTRATION NUMBER: NCT01602536.

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.

Mapping Engagement in Twitter-Based Support Networks for Adult Smoking Cessation.

We examined engagement in novel quit-smoking private social support networks on Twitter, January 2012 to April 2014. We mapped communication patterns within 8 networks of adult smokers (n = 160) with network ties defined by participants' tweets over 3 time intervals, and examined tie reciprocity, tie strength, in-degree centrality (popularity), 3-person triangles, 4-person cliques, network density, and abstinence status. On average, more than 50% of ties were reciprocated in most networks and most ties were between abstainers and nonabstainers. Tweets formed into more aggregated patterns especially early in the study. Across networks, 35.00% (7 days after the quit date), 49.38% (30 days), and 46.88% (60 days) abstained from smoking. We demonstrated that abstainers and nonabstainers engaged with one another in dyads and small groups. This study preliminarily suggests potential for Twitter as a platform for adult smoking-cessation interventions.

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.

Simulating Dynamic Network Models and Adolescent Smoking: The Impact of Varying Peer Influence and Peer Selection.

We used a stochastic actor-based approach to examine the effect of peer influence and peer selection--the propensity to choose friends who are similar--on smoking among adolescents. Data were collected from 1994 to 1996 from 2 schools involved in the National Longitudinal Study of Adolescent to Adult Health, with respectively 2178 and 976 students, and different levels of smoking. Our experimental manipulations of the peer influence and selection parameters in a simulation strategy indicated that stronger peer influence decreased school-level smoking. In contrast to the assumption that a smoker may induce a nonsmoker to begin smoking, adherence to antismoking norms may result in an adolescent nonsmoker inducing a smoker to stop smoking and reduce school-level smoking.

Research Note: The consequences of different methods for handling missing network data in Stochastic Actor Based Models.

Although stochastic actor based models (e.g., as implemented in the SIENA software program) are growing in popularity as a technique for estimating longitudinal network data, a relatively understudied issue is the consequence of missing network data for longitudinal analysis. We explore this issue in our research note by utilizing data from four schools in an existing dataset (the AddHealth dataset) over three time points, assessing the substantive consequences of using four different strategies for addressing missing network data. The results indicate that whereas some measures in such models are estimated relatively robustly regardless of the strategy chosen for addressing missing network data, some of the substantive conclusions will differ based on the missing data strategy chosen. These results have important implications for this burgeoning applied research area, implying that researchers should more carefully consider how they address missing data when estimating such models.

Development of a Twitter-based intervention for smoking cessation that encourages high-quality social media interactions via automessages.

BACKGROUND: The medical field seeks to use social media to deliver health interventions, for example, to provide low-cost, self-directed, online self-help groups. However, engagement in online groups is often low and the informational content may be poor. OBJECTIVE: The specific study aims were to explore if sending automessages to online self-help groups encouraged engagement and to see if overall or specific types of engagement related to abstinence. METHODS: We conducted a Stage I Early Therapy Development Trial of a novel social media intervention for smoking cessation called Tweet2Quit that was delivered online over closed, 20-person quit-smoking groups on Twitter in 100 days. Social media such as Twitter traditionally involves non-directed peer-to-peer exchanges, but our hybrid social media intervention sought to increase and direct such exchanges by sending out two types of autocommunications daily: (1) an "automessage" that encouraged group discussion on an evidence-based cessation-related or community-building topic, and (2) individualized "autofeedback" to each participant on their past 24-hour tweeting. The intervention was purposefully designed without an expert group facilitator and with full automation to ensure low cost, easy implementation, and broad scalability. This purely Web-based trial examined two online quit-smoking groups with 20 members each. Participants were adult smokers who were interested in quitting and were recruited using Google AdWords. Participants' tweets were counted and content coded, distinguishing between responses to the intervention's automessages and spontaneous tweets. In addition, smoking abstinence was assessed at 7 days, 30 days, and 60 days post quit date. Statistical models assessed how tweeting related to abstinence. RESULTS: Combining the two groups, 78% (31/40) of the members sent at least one tweet; and on average, each member sent 72 tweets during the 100-day period. The automessage-suggested discussion topics and participants' responses to those daily automessages were related in terms of their content (r=.75, P=.012). Responses to automessages contributed 22.78% (653/2867) of the total tweets; 77.22% (2214/2867) were spontaneous. Overall tweeting related only marginally to abstinence (OR 1.03, P=.086). However, specific tweet content related to abstinence including tweets about setting of a quit date or use of nicotine patches (OR 1.52, P=.024), countering of roadblocks to quitting (OR 1.76, P=.008) and expressions of confidence about quitting (OR 1.71, SE 0.42, P=.032). Questionable, that is, non-evidence-based, information about quitting did not relate to abstinence (OR 1.12, P=.278). CONCLUSIONS: A hybrid social media intervention that combines traditional online social support with daily automessages appears to hold promise for smoking cessation. This hybrid approach capitalizes on social media's spontaneous real-time peer-to-peer exchanges but supplements this with daily automessages that group members respond to, bolstering and sustaining the social network and directing the information content. Highly engaging, this approach should be studied further. TRIAL REGISTRATION: Clinicaltrials.gov NCT01602536; https://clinicaltrials.gov/ct2/show/NCT01602536 (Archived by WebCite at http://www.webcitation.org/6WGbt0o1K).

A Dynamic Model of Adolescent Friendship Networks, Parental Influences, and Smoking.

Peer and parental influences are critical socializing forces shaping adolescent development, including the co-evolving processes of friendship tie choice and adolescent smoking. This study examines aspects of adolescent friendship networks and dimensions of parental influences shaping friendship tie choice and smoking, including parental support, parental monitoring, and the parental home smoking environment using a Stochastic Actor-Based model. With data from three waves of the National Longitudinal Study of Adolescent Health of youth in grades 7 through 12, including the In-School Survey, the first wave of the In-Home survey occurring 6 months later, and the second wave of the In-Home survey, occurring one year later, this study utilizes two samples based on the social network data collected in the longitudinal saturated sample of sixteen schools. One consists of twelve small schools (n = 1,284, 50.93 % female), and the other of one large school (n = 976, 48.46 % female). The findings indicated that reciprocity, choosing a friend of a friend as a friend, and smoking similarity increased friendship tie choice behavior, as did parental support. Parental monitoring interacted with choosing friends who smoke in affecting friendship tie choice, as at higher levels of parental monitoring, youth chose fewer friends that smoked. A parental home smoking context conducive to smoking decreased the number of friends adolescents chose. Peer influence and a parental home smoking environment conducive to smoking increased smoking, while parental monitoring decreased it in the large school. Overall, peer and parental factors affected the coevolution of friendship tie choice and smoking, directly and multiplicatively.

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.

Social network tie functions of social support and social influence and adult smoking abstinence.

Adults' social network ties serve multiple functions and play prominently in quitting smoking. We examined three types of adults' egocentric social networks, including family, friends, and friends online to investigate how two network characteristics with major relevance to health behavior, network size and tie closeness, related to the emotional and confidant support and to pro- and anti-smoking social influence these ties may transmit. We also examine whether the social support and social influence constructs related to smoking abstinence. We utilized baseline and 7-day abstinence survey data from 123 adult current smokers attempting to quit prior to the start of a randomized controlled quit-smoking trial of a social support intervention for quitting smoking on Twitter. To examine study relationships, we estimated Negative Binomial Regression models and Logistic Regression models. For all networks, network size and tie closeness related positively to most of the social support and social influence constructs, with tie closeness related most strongly, especially for online friends. Family pro-smoking social influence related negatively to smoking abstinence, and there were marginally negative relationships for family emotional support and family confidant support. Online friend emotional support had a marginally positive relationship with smoking abstinence. Overall, our findings indicated the importance of the social support and social influence functions of each type of network tie, with larger networks and closer ties related to higher levels of social support and social influence. Moreover, family network pro-smoking social influence may compromise abstinence while emotional support from online friend network ties may reinforce it.

Insurance Status, Comorbidity Diagnosis, and Hepatitis C Diagnosis Among Antibody-Positive Patients: A Retrospective Cohort Study.

Background: In California, laboratories report all hepatitis C (HCV)-positive antibody tests to the state; however, that does not accurately reflect active infection among those patients without a viral load test confirming a patient's HCV diagnosis. These public health surveillance disease incident records do not include patient details such as comorbidities or insurance status found in electronic medical records (EMRs). Objective: This research seeks to understand how insurance type, insurance status, patient comorbidities, and other sociodemographic factors related to HCV diagnosis as defined by a positive viral load test among HCV antibody-positive persons from January 1, 2010 to March 1, 2020. Methods: HCV antibody-positive individuals reported to the California Reportable Disease Information Exchange (CalREDIE), with a medical record number associated with the University of California, Irvine Medical Center, and an unrestricted EMR (n = 521) were extracted using manual chart review. Main Outcomes and measures: HCV diagnosis as indicated in a patient's EMR in the problem list or disease registry. Results: Less than a quarter of patients in this sample were diagnosed as having HCV in their EMR, with 0.4% of those diagnosed (5/116) patients with indicated HCV treatment in the medication field of their charts. After adjusting for multiple comorbidities, a multinomial logistic regression found that the relative risk ratios (RRRs) of HCV diagnosis found that patients with insurance were more likely to be diagnosed compared to those without insurance. When comparing uninsured patients to those with government insurance at the P < .05 level (RRR = 10.61 (95% confidence interval (CI): 4.14-27.22)) and those uninsured to private insurance (RRR = 6.79 (95% CI: 2.31-19.92). Conclusions: These low frequencies of HCV diagnosis among the study population, particularly among the uninsured, indicate a need for increased viral load testing and linkage to care. Reflex testing on existing samples and improving HCV screening and diagnosis can help increase linkage to care and work towards eliminating this disease.

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.

Measuring Hazards of Undetectable Viral Load among Hepatitis C Antibody Positive Residents of a Large Southern California County.

Background Hepatitis C virus (HCV) infection is the most common bloodborne infection in the U.S. However, only a small proportion of persons are treated and cured. Previous research has not characterized sociodemographic characteristics of who receives treatment. We examined predictors of undetectable for HCV in Orange County, the sixth largest county in the United States, where HCV is the most commonly reported infection. METHODS: From 2014 to 2020, we acquired public health surveillance data from 91,165 HCV antibody-positive care encounters from the California Reportable Disease Information Exchange (CalREDIE). We used a time-to-event proportional hazards framework to estimate individual and area-level correlates of time-to-HCV undetectable viral load among HCV + individuals. RESULTS: Older adults (>65 years) showed an increased hazard of undetectable viral load relative to younger adults (HR = 2.00). In addition, residents of census tracts with greater enrollment in health insurance showed a greater likelihood of undetectable viral load (HR = 1.36). The moderating effect of higher tract median household income and higher tract levels of health insurance were more likely to have undetectable viral load and was statistically significant. CONCLUSION: In a large urban county, HCV antibody-positive older adults appear much more likely to show undetectable viral load compared to younger adults. Residents in areas with higher quartiles of health insurance enrollment have an increased likelihood of undetectable viral load. The extent to which constraints impede HCV care requires further investigation, including follow-up studies on health insurance type to test the relationship of health insurance type to undetectable viral load.

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.

The social context of adolescent smoking: a systems perspective.

We used a systems science perspective to examine adolescents' personal networks, school networks, and neighborhoods as a system through which emotional support and peer influence flow, and we sought to determine whether these flows affected past-month smoking at 2 time points, 1994-1995 and 1996. To test relationships, we employed structural equation modeling and used public-use data from the National Longitudinal Study of Adolescent Health (n = 6504). Personal network properties affected past-month smoking at both time points via the flow of emotional support. We observed a feedback loop from personal network properties to emotional support and then to past-month smoking. Past-month smoking at time 1 fed back to positively affect in-degree centrality (i.e., popularity). Findings suggest that networks and neighborhoods in this system positively affected past-month smoking via flows of emotional support.

Use of propensity score matching in evaluating smokeless tobacco as a gateway to smoking.

INTRODUCTION: The contentious debate over the promotion of Swedish snus, a form of moist snuff, as an alternative to cigarettes has often focused on the product's potential as a gateway to smoking. Findings from prospective studies in the United States have suggested that smokeless tobacco (SLT) is a significant predictor of smoking onset, before and after adjustment for baseline covariates. Use of conventional regression methods in these studies may have resulted in biased parameter estimates, arising from imbalanced covariate distributions in the users and nonusers of SLT. An alternative approach, which has been used widely in the econometric literature, matches exposure or treatment levels on the basis of the propensity score distribution. METHODS: Using this approach, we matched current SLT users from the National Longitudinal Study of Adolescent Health with nonusers (496 pairs) and followed them from adolescence into young adulthood for determination of smoking status. RESULTS: Prior to matching, the unadjusted risk of becoming a daily smoker was significantly greater for the SLT users compared with nonusers (n = 10,820; range of relative risk = 1.3-2.0, p < .001). However, after pairing individuals on propensity score, we found no evidence for an increased risk of smoking among the SLT users. DISCUSSION: Baseline differences in the risk factors for smoking likely account for the association between the two tobacco products.

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.

Measuring social integration and tie strength with smartphone and survey data.

Recordings of smartphone use for contacts are increasingly being used as alternative or supplementary measurement methods for social interactions and social relations in the health sciences. Less work has been done to understand how these measures compare with widely used survey-based information. Using data from the Copenhagen Network Study, we investigated whether derived survey and smartphone measures on two widely studied concepts; Social integration and Tie strength were associated. The study population included 737 college students (mean age 21.6 years, Standard deviation: 2.6), who were followed with surveys and continuous recordings of smartphone usage over a one-month period. We derived self-reported and smartphone measures of social integration (social role diversity, social network size), and tie strength (contact frequency, duration and tie reciprocity). Logistic regression models were used to assess the associations between smartphone derived and self-reported measures adjusting for gender, age and co-habitation. Larger call and text message networks were associated with having a high self-reported social role diversity, and a high self-reported social contact frequency was likewise associated with having both frequent call and text message interactions, longer call duration and a higher level of reciprocity in call and text message communication. Self-reported aspects of social relations and smartphone measures of social interactions have considerable overlap supporting a measurement of similar underlying concepts.