Social networks and the spread of infectious diseases: the AIDS example

Bridges and Mechanisms: Integrating Systems Science Thinking into Implementation Research

We present a detailed argument for how to integrate, or bridge, systems science thinking and methods with implementation science. We start by showing how fundamental systems science principles of structure, dynamics, information, and utility are relevant for implementation science. Then we examine the need for implementation science to develop and apply richer theories of complex systems. This can be accomplished by emphasizing a causal mechanisms approach. Identifying causal mechanisms focuses on the "cogs and gears" of public health, clinical, and organizational interventions. A mechanisms approach focuses on how a specific strategy will produce the implementation outcome. We show how connecting systems science to implementation science opens new opportunities for examining and addressing social determinants of health and conducting equitable and ethical implementation research. Finally, we present case studies illustrating successful applications of systems science within implementation science in community health policy, tobacco control, health care access, and breast cancer screening.

Sociodemographic Variables Can Guide Prioritized Testing Strategies for Epidemic Control in Resource-Limited Contexts

BACKGROUND

Targeted surveillance allows public health authorities to implement testing and isolation strategies when diagnostic resources are limited, and can be implemented via the consideration of social network topologies. However, it remains unclear how to implement such surveillance and control when network data are unavailable.

METHODS

We evaluated the ability of sociodemographic proxies of degree centrality to guide prioritized testing of infected individuals compared to known degree centrality. Proxies were estimated via readily available sociodemographic variables (age, gender, marital status, educational attainment, household size). We simulated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics via a susceptible-exposed-infected-recovered individual-based model on 2 contact networks from rural Madagascar to test applicability of these findings to low-resource contexts.

RESULTS

Targeted testing using sociodemographic proxies performed similarly to targeted testing using known degree centralities. At low testing capacity, using proxies reduced infection burden by 22%-33% while using 20% fewer tests, compared to random testing. By comparison, using known degree centrality reduced the infection burden by 31%-44% while using 26%-29% fewer tests.

CONCLUSIONS

We demonstrate that incorporating social network information into epidemic control strategies is an effective countermeasure to low testing capacity and can be implemented via sociodemographic proxies when social network data are unavailable.

Health behavior homophily can mitigate the spread of infectious diseases in small-world networks

Research has repeatedly shown that the spread of infectious diseases is influenced by properties of our social networks. Small-world like structures with densely connected clusters bridged by only a few connections, for example, are not only known to diminish disease spread, but also to increase the chance for a disease to spread to any part of the network. Clusters composed of individuals who show similar reactions to avoid infections (health behavior homophily), however, might change the effect of such clusters on disease spread. To study the combined effect of health behavior homophily and small-world network properties on disease spread, we extend a previously developed ego-centered network formation model and agent-based simulation. Based on more than 80,000 simulated epidemics on generated networks varying in clustering and homophily, as well as diseases varying in severity and infectivity, we predict that the existence of health behavior homophilous clusters reduce the number of infections, lower peak size, and flatten the curve of active cases. That is because agents perceiving higher risks of infections can protect their cluster from infections comparatively quickly by severing only a few bridging ties. A comparison with epidemics in static network structures shows that the incapability to act upon risk perceptions and the low connectivity between clusters in static networks lead to diametrically opposed effects with comparatively large epidemics and prolonged epidemics. These finding suggest that micro-level behavioral adaptation to health risks mitigate macro-level disease spread to an extent that is not captured by static network models of disease spread. Furthermore, this mechanism can be used to design information campaigns targeting proxies for groups with lower risk perception.

Contribution of socio-economic factors in the spread of antimicrobial resistant infections in Australian primary healthcare clinics

OBJECTIVES

To effectively contain antimicrobial resistant (AMR) infections, we must better understand the social determinates of health that contribute to transmission and spread of infections.

METHODS

We used clinical data from patients attending primary healthcare clinics across three jurisdictions of Australia (2007-2019). Escherichia coli (E. coli), Klebsiella pneumoniae (K. pneumoniae), Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus) isolates and their corresponding antibiotic susceptibilities were included. Using multivariable logistic regression analysis, we assessed associations between AMR prevalence and indices of social disadvantage as reported by the Australian Bureau of Statistics (i.e. remoteness, socio-economic disadvantage and average person per household).

RESULTS

This study reports 12 years of longitudinal data from 43,448 isolates from a high-burden low resource setting in Australia. Access to health and social services (as measured by remoteness index) was a risk factor for increased prevalence of third-generation cephalosporin-resistant (3GC) E. coli (odds ratio 5.05; 95% confidence interval 3.19, 8.04) and methicillin-resistant S. aureus (MRSA) (odds ratio 5.72; 95% confidence interval 5.02, 6.54). We did not find a positive correlation of AMR and socio-economic disadvantage or average person per household indices.

CONCLUSIONS

Remoteness is a risk factor for increased prevalence of 3GC-resistant E.coli and MRSA. We demonstrate that traditional disease surveillance systems can be repurposed to capture the broader social drivers of AMR. Access to pathogen-specific and social data early and within the local regional context will fill a significant gap in disease prevention and the global spread of AMR.

Analytic Advances in Social Networks and Health in the Twenty-First Century

The study of social networks is increasingly central to health research for medical sociologists and scholars in other fields. Here, we review the innovations in theory, substance, data collection, and methodology that have propelled the study of social networks and health from a niche subfield to the center of larger sociological and scientific debates. In particular, we contextualize the broader history of network analysis and its connections to health research, concentrating on work beginning in the late 1990s, much of it in this journal. Using bibliometric and network visualization approaches, we examine the subfield's evolution over this period in terms of topics, trends, key debates, and core insights. We conclude by reflecting on persistent challenges and areas of innovation shaping the study of social networks and health and its intersection with medical sociology in the coming years.

Beyond HIV outbreaks: protocol, rationale and implementation of a prospective study quantifying the benefit of incorporating viral sequence clustering analysis into routine public health interventions

INTRODUCTION

HIV continues to have great impact on millions of lives. Novel methods are needed to disrupt HIV transmission networks. In the USA, public health departments routinely conduct contact tracing and partner services and interview newly HIV-diagnosed index cases to obtain information on social networks and guide prevention interventions. Sequence clustering methods able to infer HIV networks have been used to investigate and halt outbreaks. Incorporation of such methods into routine, not only outbreak-driven, contact tracing and partner services holds promise for further disruption of HIV transmissions.

METHODS AND ANALYSIS

Building on a strong academic-public health collaboration in Rhode Island, we designed and have implemented a state-wide prospective study to evaluate an intervention that incorporates real-time HIV molecular clustering information with routine contact tracing and partner services. We present the rationale and study design of our approach to integrate sequence clustering methods into routine public health interventions as well as related important ethical considerations. This prospective study addresses key questions about the benefit of incorporating a clustering analysis triggered intervention into the routine workflow of public health departments, going beyond outbreak-only circumstances. By developing an intervention triggered by, and incorporating information from, viral sequence clustering analysis, and evaluating it with a novel design that avoids randomisation while allowing for methods comparison, we are confident that this study will inform how viral sequence clustering analysis can be routinely integrated into public health to support the ending of the HIV pandemic in the USA and beyond.

ETHICS AND DISSEMINATION

The study was approved by both the Lifespan and Rhode Island Department of Health Human Subjects Research Institutional Review Boards and study results will be published in peer-reviewed journals.

Complex network analysis to understand trading partnership in French swine production

The circulation of livestock pathogens in the pig industry is strongly related to animal movements. Epidemiological models developed to understand the circulation of pathogens within the industry should include the probability of transmission via between-farm contacts. The pig industry presents a structured network in time and space, whose composition changes over time. Therefore, to improve the predictive capabilities of epidemiological models, it is important to identify the drivers of farmers’ choices in terms of trade partnerships. Combining complex network analysis approaches and exponential random graph models, this study aims to analyze patterns of the swine industry network and identify key factors responsible for between-farm contacts at the French scale. The analysis confirms the topological stability of the network over time while highlighting the important roles of companies, types of farm, farm sizes, outdoor housing systems and batch-rearing systems. Both approaches revealed to be complementary and very effective to understand the drivers of the network. Results of this study are promising for future developments of epidemiological models for livestock diseases. This study is part of the One Health European Joint Programme: BIOPIGEE.

JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook

We use aggregated data from Facebook to show that COVID-19 is more likely to spread between regions with stronger social network connections. Areas with more social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally had more confirmed COVID-19 cases by the end of March. These relationships hold after controlling for geographic distance to the hotspots as well as the population density and demographics of the regions. As the pandemic progressed in the U.S., a county's social proximity to recent COVID-19 cases and deaths predicts future outbreaks over and above physical proximity and demographics. In part due to its broad coverage, social connectedness data provides additional predictive power to measures based on smartphone location or online search data. These results suggest that data from online social networks can be useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.

The First 40 Years of AIDS: Promising Programs, Limited Success

HIV-prevention program planning, implementation, and evaluation began in the United States shortly after reports of a mysterious, apparently acquired, immune deficiency syndrome appeared in summer 1981. In San Francisco, New York City, and elsewhere, members of LGBT communities responded by providing accurate information, giving support, and raising money. During the first decade of the AIDS pandemic (1981–1990), social and behavioral scientists contributed by designing theory-based and practical interventions, combining interventions into programs, and measuring impact on behavior change and HIV incidence. In the second decade (1991–2000), federal, state, and local agencies and organizations played a more prominent role in establishing policies and procedures, funding research and programs, and determining the direction of intervention efforts. In the third decade (2001–2010), biomedical interventions were prioritized over behavioral interventions and have dominated attempts in the fourth decade (2011–2020) to integrate biomedical, behavioral, and structural interventions into coherent, efficient, and cost-effective programs to end AIDS.

Privacy and Confidentiality Considerations for Collecting HIV Risk Network Data among Men who Have Sex with Men and Implications for Constructing Valid Risk Networks

Studies aiming to construct risk networks have historically collected network members' names, demographic characteristics and relational data (i.e., type, strength, duration, frequency of interaction, and HIV-related risk behaviors between the pair). Due to difficulties in constructing risk networks stemming from partner anonymity and the use of nicknames, some studies also collect network members' screen names, phone numbers, physical attributes, and scars/tattoos to assist with entity resolution. In-depth interviews with 20 men who have sex with men and transgender women in Kentucky assessed privacy/confidentiality concerns as well as accuracy/recall issues associated with providing these details. Most preferred providing alters' nicknames/first names only or a first name with the first letter of the last name. Many perceived screen names, phone numbers, and scars/tattoos to be too personal/identifying. Willingness to provide more detailed information varied by relationship type/strength, which could influence the validity of the resulting network.

Association between tuberculosis in men and social network structure in Kampala, Uganda

Background

Globally, tuberculosis disease (TB) is more common among males than females. Recent research proposes that differences in social mixing by sex could alter infection patterns in TB. We examine evidence for two mechanisms by which social-mixing could increase men’s contact rates with TB cases. First, men could be positioned in social networks such that they contact more people or social groups. Second, preferential mixing by sex could prime men to have more exposure to TB cases.

Methods

We compared the networks of male and female TB cases and healthy matched controls living in Kampala, Uganda. Specifically, we estimated their positions in social networks (network distance to TB cases, degree, betweenness, and closeness) and assortativity patterns (mixing with adult men, women, and children inside and outside the household).

Results

The observed network consisted of 11,840 individuals. There were few differences in estimates of node position by sex. We found distinct mixing patterns by sex and TB disease status including that TB cases have proportionally more adult male contacts and fewer contacts with children.

Conclusions

This analysis used a network approach to study how social mixing patterns are associated with TB disease. Understanding these mechanisms may have implications for designing targeted intervention strategies in high-burden populations.

Understanding material and supplier networks in the construction of disaster-relief shelters: the feasibility of using social network analysis as a decision-making tool

Purpose

Understanding the supply network of construction materials used to construct shelters in refugee camps, or during the reconstruction of communities, is important as it can reveal the intricate links between different stakeholders and the volumes and speeds of material flows to the end-user. Using social network analysis (SNA) enables another dimension to be analysed – the role of commonalities. This is likely to be particularly important when attempting to replace vernacular materials with higher-performing alternatives or when encouraging the use of non-vernacular methods. This paper aims to analyse the supply networks of four different disaster-relief situations.

Design/methodology/approach

Data were collected from interviews with 272 displaced (or formally displaced) families in Afghanistan, Bangladesh, Nepal and Turkey, often in difficult conditions.

Findings

The results show that the form of the supply networks was highly influenced by the nature/cause of the initial displacement, the geographical location, the local availability of materials and the degree of support/advice given by aid agencies and or governments. In addition, it was found that SNA could be used to indicate which strategies might work in a particular context and which might not, thereby potentially speeding up the delivery of novel solutions.

Research limitations/implications

This study represents the first attempt in theorising and empirically investigating supply networks using SNA in a post-disaster reconstruction context. It is suggested that future studies might map the up-stream supply chain to include manufacturers and higher-order, out of country, suppliers. This would provide a complete picture of the origins of all materials and components in the supply network.

Originality/value

This is original research, and it aims to produce new knowledge.

Analysis of social interactions and risk factors relevant to the spread of infectious diseases at hospitals and nursing homes

Ensuring the safety of healthcare workers is vital to overcome the ongoing COVID-19 pandemic. We here present an analysis of the social interactions between the healthcare workers at hospitals and nursing homes. Using data from an automated hand hygiene system, we inferred social interactions between healthcare workers to identify transmission paths of infection in hospitals and nursing homes. A majority of social interactions occurred in medication rooms and kitchens emphasising that health-care workers should be especially aware of following the infection prevention guidelines in these places. Using epidemiology simulations of disease at the locations, we found no need to quarantine all healthcare workers at work with a contagious colleague. Only 14.1% and 24.2% of the health-care workers in the hospitals and nursing homes are potentially infected when we disregard hand sanitization and assume the disease is very infectious. Based on our simulations, we observe a 41% and 26% reduction in the number of infected healthcare workers at the hospital and nursing home, when we assume that hand sanitization reduces the spread by 20% from people to people and 99% from people to objects. The analysis and results presented here forms a basis for future research to explore the potential of a fully automated contact tracing systems.

The impact of dengue illness on social distancing and caregiving behavior

BACKGROUND

Human mobility among residential locations can drive dengue virus (DENV) transmission dynamics. Recently, it was shown that individuals with symptomatic DENV infection exhibit significant changes in their mobility patterns, spending more time at home during illness. This change in mobility is predicted to increase the risk of acquiring infection for those living with or visiting the ill individual. It has yet to be considered, however, whether social contacts are also changing their mobility, either by socially distancing themselves from the infectious individual or increasing contact to help care for them. Social, or physical, distancing and caregiving could have diverse yet important impacts on DENV transmission dynamics; therefore, it is necessary to better understand the nature and frequency of these behaviors including their effect on mobility.

METHODOLOGY AND PRINCIPAL FINDINGS

Through community-based febrile illness surveillance and RT-PCR infection confirmation, 67 DENV positive (DENV+) residents were identified in the city of Iquitos, Peru. Using retrospective interviews, data were collected on visitors and home-based care received during the illness. While 15% of participants lost visitors during their illness, 22% gained visitors; overall, 32% of all individuals (particularly females) received visitors while symptomatic. Caregiving was common (90%), particularly caring by housemates (91%) and caring for children (98%). Twenty-eight percent of caregivers changed their behavior enough to have their work (and, likely, mobility patterns) affected. This was significantly more likely when caring for individuals with low "health-related quality of well-being" during illness (Fisher's Exact, p = 0.01).

CONCLUSIONS/SIGNIFICANCE

Our study demonstrates that social contacts of individuals with dengue modify their patterns of visitation and caregiving. The observed mobility changes could impact a susceptible individual's exposure to virus or a presymptomatic/clinically inapparent individual's contribution to onward transmission. Accounting for changes in social contact mobility is imperative in order to get a more accurate understanding of DENV transmission.

Livestock Network Analysis for Rhodesiense Human African Trypanosomiasis Control in Uganda

Background: Infected cattle sourced from districts with established foci for Trypanosoma brucei rhodesiense human African trypanosomiasis (rHAT) migrating to previously unaffected districts, have resulted in a significant expansion of the disease in Uganda. This study explores livestock movement data to describe cattle trade network topology and assess the effects of disease control interventions on the transmission of rHAT infectiousness. Methods: Network analysis was used to generate a cattle trade network with livestock data which was collected from cattle traders (n = 197) and validated using random graph methods. Additionally, the cattle trade network was combined with a susceptible, infected, recovered (SIR) compartmental model to simulate spread of rHAT (R o 1.287), hence regarded as "slow" pathogen, and evaluate the effects of disease interventions. Results: The cattle trade network exhibited a low clustering coefficient (0.5) with most cattle markets being weakly connected and a few being highly connected. Also, analysis of the cattle movement data revealed a core group comprising of cattle markets from both eastern (rHAT endemic) and northwest regions (rHAT unaffected area). Presence of a core group may result in rHAT spread to unaffected districts and occurrence of super spreader cattle market or markets in case of an outbreak. The key cattle markets that may be targeted for routine rHAT surveillance and control included Namutumba, Soroti, and Molo, all of which were in southeast Uganda. Using effective trypanosomiasis such as integrated cattle injection with trypanocides and spraying can sufficiently slow the spread of rHAT in the network. Conclusion: Cattle trade network analysis indicated a pathway along which T. b. rhodesiense could spread northward from eastern Uganda. Targeted T. b. rhodesiense surveillance and control in eastern Uganda, through enhanced public-private partnerships, would serve to limit its spread.

Linking Self-Reported Social Distancing to Real-World Behavior During the COVID-19 Pandemic

In an effort to combat COVID-19 and future pandemics, researchers have attempted to identify the factors underlying social distancing. Yet, much of this research relies on self-report measures. In two studies, we examine whether self-reported social distancing predicts objective distancing behavior. In Study 1, individuals’ self-reported social distancing predicted decreased mobility (assessed via smartphone step counts) during the COVID-19 pandemic. While participants high in self-reported distancing (+1 SD) exhibited a 33% reduction in daily step counts, those low in distancing (−1 SD) exhibited only a 3% reduction. Study 2 extended these findings to the group level. Self-reported social distancing at the U.S. state level accounted for 20% of the variance in states’ objective reduction in overall movement and visiting nonessential services (calculated via the GPS coordinates of ∼15 million people). Collectively, our results indicate that self-reported social distancing tracks actual social distancing behavior.

Sociometric Risk Network Structure, HIV Prevalence, and Drug Injection-Related Norms among People Who Inject Drugs (PWID) in Athens, Greece

Background: Structural properties of sociometric networks have been associated with behaviors related to HIV transmission. Very few studies, however, have explored the correlation between sociometric network factors and drug injection-related norms. Methods: This exploratory work: (i) describes basic structural qualities of a sociometric risk network of participants in the Transmission Reduction Intervention Project (TRIP) in Athens, Greece, in the context of a large HIV outbreak among people who inject drugs (PWID); (ii) measures HIV prevalence within specific structures within the sociometric risk network of PWID in TRIP; and (iii) explores the association of structural properties of the sociometric risk network in TRIP with drug injection-related norms. Results: The sociometric risk network in TRIP consisted of a large component (n = 241, 67.8%), a few small components (n = 36, 10.1%) with 2-10 individuals each, and some isolates (n = 79, 22.2%). HIV prevalence was significantly higher in the large component (55.6%), the 2-core (59.1%) and 3-core (66.3%) of the large component, and the 3-cliques of the cores. Drug injection-related norms were significantly associated with structural characteristics of the sociometric risk network. A safe behavioral pattern (use of unclean cooker/filter/rinse water was never encouraged) was significantly (p = 0.03) less normative among people who TRIP participants of the 2-core injected with (40.5%) than among network contacts of TRIP participants outside the 2-core (55.6%). On the contrary, at drug-using venues, 2-core members reported that safer behaviors were normative compared to what was reported by those without 2-core membership. Conclusions: Sociometric network data can give useful insights into HIV transmission dynamics and inform prevention strategies.Supplemental data for this article is available online at https://doi.org/10.1080/10826084.2021.1914103 .

Advances in research of HIV transmission networks

Transmission network analysis is a crucial evaluation tool aiming to explore the characteristics of the human immunodeficiency virus epidemic, develop evidence-based prevention strategies, and contribute to various areas of human immunodeficiency virus/acquired immunodeficiency syndrome prevention and control. Over recent decades, transmission networks have made tremendous strides in terms of modes, methods, applications, and various other aspects. Transmission network methods, including social, sexual, and molecular transmission networks, have played a pivotal role. Each transmission network research method has its advantages, as well as its limitations. In this study, we established a systematic review of these aforementioned transmission networks with respect to their definitions, applications, limitations, recent progress, and synthetic applications.

Social network support and harm reduction activities in a peer researcher-led pilot study, British Columbia, Canada

Background

People who smoke drugs (PWSD) are at high risk of both infectious disease and overdose. Harm reduction activities organized by their peers in the community can reduce risk by providing education, safer smoking supplies, and facilitate access to other services. Peers also provide a network of people who provide social support to PWSD which may reinforce harm reducing behaviors. We evaluated the numbers of supportive network members and the relationships between received support and participants’ harm-reducing activities.

Methods

Initial peer-researchers with past or current lived drug use experience were employed from communities in Abbotsford and Vancouver to interview ten friends from their social networks who use illegal drugs mainly through smoking. Contacts completed a questionnaire about people in their own harm reduction networks and their relationships with each other. We categorized social support into informational, emotional, and tangible aspects, and harm reduction into being trained in the use of, or carrying naloxone, assisting peers with overdoses, using brass screens to smoke, obtaining pipes from service organizations and being trained in CPR.

Results

Fifteen initial peer researchers interviewed 149 participants who provided information on up to 10 people who were friends or contacts and the relationships between them. People who smoked drugs in public were 1.46 (95% CI, 1.13-1.78) more likely to assist others with possible overdoses if they received tangible support; women who received tangible support were 1.24 (95% CI; 1.02-1.45) more likely to carry and be trained in the use of naloxone. There was no relationship between number of supportive network members and harm reduction behaviors.

Conclusions

In this pilot study, PWSD who received tangible support were more likely to assist peers in possible overdoses and be trained in the use of and/or carry naloxone, than those who did not receive tangible support. Future work on the social relationships of PWSD may prove valuable in the search for credible and effective interventions.

A New Model for Epidemic Spreading with a Focus on COVID-19

Background: It is extremely useful to construct mathematical models to forecast and control real phenomena. One of the common applied statistical models to represent the data involving with time is the time series modeling. A novel time series model to represent the propagation of an epidemic infection in a population is presented. The model deals with addressing the cumulative number of confirmed cases. Methods: Our model is the generalization of statistical exponential growth models and can describe different stages of the outbreak of a communicable disease. Applying the mentioned procedure leads to models CVJR1 (3.2, 1.44, 3, 13) for modeling the sequence of COVID-19 from January 13 to March 5. All computations and 200 simulations were done in MatLab 8.6. Results: For comparing candidates through fitting the dataset for six pairs of (l^ and a^), we used the minimum criterion square of residuals. We present the average and 90% upper and lower bounds of the predictions made by our models for three periods. Applying the mentioned procedure led to having models with parameters (3.2, 1.44, 3, 13) for modeling the course of COVID-19 from January 13 to March 5. Conclusions: The presented model can cover the epidemic behaviors related to social networks. Our model can be adjusted to worldwide modeling for modeling a phenomenon spreading in different populations simultaneously.

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.

Statistical methods for the estimation of contagion effects in human disease and health networks

Contagion effects, sometimes referred to as spillover or influence effects, have long been central to the study of human disease and health networks. Accurate estimation and identification of contagion effects are important in terms of understanding the spread of human disease and health behavior, and they also have various implications for designing effective public health interventions. However, many challenges remain in estimating contagion effects and it is often unclear when it is difficult to correctly estimate contagion effects, or why a particular method would need to be applied. In this review I explain the challenges in estimating contagion effects, and how they can be framed as an omitted variable bias problem. I then discuss how such challenges have been addressed in randomized experiments and traditional statistical analyses, as well as several state-of-the-art statistical methods. Finally, I conclude by summarizing recent advancements and noting remaining challenges, as well as appropriate next steps.

Preventing Internal COVID-19 Outbreaks within Businesses and Institutions: A Methodology Based on Social Networks Analysis for Supporting Occupational Health and Safety Services Decision Making

This study aims at developing and demonstrating in a real case study a methodology for supporting Occupational Health and Safety Services in the design and assessment of preventive measures to reduce the risks of COVID-19 outbreaks within their entities. The proposed methodology applies the concepts from Social Network Analysis (SNA) to the current challenge of preventing risks of contagion of viruses like SARS-COV-2 among employees. For this purpose, the authors consider a network of employees whose interaction is caused by triggers, which are defined as common circumstances between two workers that may result in contagion, like sharing an office or participating in the same management board. The network cohesion is then evaluated, and those core nodes, which are the most significant contributors to its integration, are identified to be addressed in the design of the preventive measures. The impact of the designed preventive measures on the networks’ cohesion is assessed for its prioritization and further deployment. The methodology has been demonstrated in a real case, a Spanish Research Center, providing promising results in a quick and easy manner. The objective insights provided by its application were demonstrated as very valuable for the Occupational Health and Safety Services in the design and evaluation of the set of preventing measures to be implemented before the return of the employees to the facilities after the Spanish confinement period. The current COVID-19 outbreak brings the need to develop tools and methods to support businesses and institutions in the use of SNA for preventing outbreaks among their employees. Although some literature does exist in the field of SNA application in epidemiology, its adaptation for extensive use by the Occupational and Health Services is still a challenge.

Pretreatment HIV drug resistance spread within transmission clusters in Mexico City

BACKGROUND

Pretreatment HIV drug resistance (HIVDR) to NNRTIs has consistently increased in Mexico City during the last decade.

OBJECTIVES

To infer the HIV genetic transmission network in Mexico City to describe the dynamics of the local HIV epidemic and spread of HIVDR.

PATIENTS AND METHODS

HIV pol sequences were obtained by next-generation sequencing from 2447 individuals before initiation of ART at the largest HIV clinic in Mexico City (April 2016 to June 2018). Pretreatment HIVDR was estimated using the Stanford algorithm at a Sanger-like threshold (≥20%). Genetic networks were inferred with HIV-TRACE, establishing putative transmission links with genetic distances <1.5%. We examined demographic associations among linked individuals with shared drug resistance mutations (DRMs) using a ≥ 2% threshold to include low-frequency variants.

RESULTS

Pretreatment HIVDR reached 14.8% (95% CI 13.4%-16.2%) in the cohort overall and 9.6% (8.5%-10.8%) to NNRTIs. Putative links with at least one other sequence were found for 963/2447 (39%) sequences, forming 326 clusters (2-20 individuals). The inferred network was assortative by age and municipality (P < 0.001). Clustering individuals were younger [adjusted OR (aOR) per year = 0.96, 95% CI 0.95-0.97, P < 0.001] and less likely to include women (aOR = 0.46, 95% CI 0.28-0.75, P = 0.002). Among clustering individuals, 175/963 (18%) shared DRMs (involving 66 clusters), of which 66/175 (38%) shared K103N/S (24 clusters). Eight municipalities (out of 75) harboured 65% of persons sharing DRMs. Among all persons sharing DRMs, those sharing K103N were younger (aOR = 0.93, 95% CI 0.88-0.98, P = 0.003).

CONCLUSIONS

Our analyses suggest age- and geographically associated transmission of DRMs within the HIV genetic network in Mexico City, warranting continuous monitoring and focused interventions.

An investigation of transportation practices in an Ontario swine system using descriptive network analysis

The objectives of this research were to describe the contact structure of transportation vehicles and swine facilities in an Ontario swine production system, and to assess their potential contribution to possible disease transmission over different time periods. A years’ worth of data (2015) was obtained from a large swine production and data management company located in Ontario, Canada. There was a total of 155 different transportation vehicles, and 220 different farms within the study population. Two-mode networks were constructed for 1-,3-, and 7-day time periods over the entire year and were analyzed. Trends in the size of the maximum weak component and outgoing contact chain over discrete time periods were investigated using linear regression. Additionally, the number of different types of facilities with betweenness >0 and in/out degree>0 were analyzed using Poisson regression. Maximum weekly outgoing contact chain (MOCC_w) contained between 2.1% and 7.1% of the study population. This suggests a potential maximum of disease spread within this population if the disease was detected within one week. Frequency of node types within MOCC_w showed considerable variability; although nursery sites were relatively most frequent. The regression analysis of several node and network level statistics indicated a potential peak time of connectivity during the summer months and warrants further confirmation and investigation. The inclusion of transportation vehicles contributed to the linear increase in the maximum weekly weak component (MWC_w) size over time. This finding in combination with constant population dynamics, may have been driven by the differential utilization of trucks over time. Despite known limitations of maximum weak components as an estimator of possible outbreaks, this finding suggests that transportation vehicles should be included, when possible and relevant, in the evaluation of contacts between farms.

Primate Infectious Disease Ecology: Insights and Future Directions at the Human-Macaque Interface

Global population expansion has increased interactions and conflicts between humans and nonhuman primates over shared ecological space and resources. Such ecological overlap, along with our shared evolutionary histories, makes human-nonhuman primate interfaces hot spots for the acquisition and transmission of parasites. In this chapter, we bring to light the importance of human-macaque interfaces in particular as hot spots for infectious disease ecological and epidemiological assessments. We first outline the significance and broader objectives behind research related to the subfield of primate infectious disease ecology and epidemiology. We then reveal how members of the genus Macaca, being among the most socioecologically flexible and invasive of all primate taxa, live under varying degrees of overlap with humans in anthropogenic landscapes. Thus, human-macaque interfaces may favor the bidirectional exchange of parasites. We then review studies that have isolated various types of parasites at human-macaque interfaces, using information from the Global Mammal Parasite Database (GMPD: http://www.mammalparasites.org/). Finally, we elaborate on avenues through which the implementation of both novel conceptual frameworks (e.g., Coupled Systems, One Health) and quantitative network-based approaches (e.g., social and bipartite networks, agent-based modeling) may potentially address some of the critical gaps in our current knowledge of infectious disease ecology at human-primate interfaces.

Network analysis of cases with methicillin-resistant Staphylococcus aureus and controls in a large tertiary care facility

BACKGROUND

Despite increased awareness of infection control precautions, methicillin-resistant Staphylococcus aureus (MRSA) still spreads through patients and contaminated objects, causing a substantial burden of illness and cost. Our objective was to define risk factors for contracting MRSA in a tertiary health care facility using a historic case-control study and to validate health care network changes during pre-outbreak and outbreak periods.

METHODS

We conducted a case-control study using secondary data on hospitalizations where infected or colonized cases were compared with matched controls who tested negative by age, sex, and campus over 1 year. Social networks of all cases and controls were built from links joining patients to rooms, roommates, and health care providers over time.

RESULTS

Matched controls were similar to cases in comorbidity, lengths of stay, mortality, and number of roommates, rooms, and health care providers. As expected, the number of rooms and roommates increased in the outbreak by more than 50%. A timed animation of the network at one campus identified potential source patients linked to 2 rooms and many roommates, after which cases connected to those same rooms proliferated.

CONCLUSIONS

Only the network animation over time revealed possible transmission of MRSA through the network, rather than attributes measured in the traditional case control study.

Understanding the Hierarchical Relationships in Female Sex Workers' Social Networks Based on Knowledge, Attitude, and Practice

Female sex workers (FSWs) represent a high-risk population for HIV infection and transmission. In general, their fellow FSWs (peers) also play a role in their level of susceptibility to HIV/AIDS. This paper draws from interview data of 93 FSWs to construct a multi-layer FSW social network model based on their knowledge, attitude, and practice (KAP). Statistical analyses of the correlation among the three dimensions of KAP as well as their social interactions indicated that FSWs had basic knowledge of HIV/AIDS prevention but demonstrated little enthusiasm in acquiring relevant information. Their knowledge, attitude, and practice were highly positively correlated. Their attitude was more likely to be negatively influenced by their companions, while their practice was more likely to be positively affected. Besides, FSWs exhibited high homophily in KAP with their neighbors. Thus, during HIV/AIDS interventions, FSWs should receive individualized education based on their specific KAP. Considering the high level of homophily among FSWs, their propensity to be positive or negative in their KAP are significantly influenced by their companions. Making full use of peer education and social interaction-based interventions may help prevent and control the spread of HIV/AIDS.

The characteristics of mixing patterns of sexual dyads and factors correlated with condomless anal intercourse among men who have sex with men in Guangzhou, China

Background

The HIV prevalence among men who have sex with men (MSM) in China has increased yearly. This study aimed to explore the association between the characteristics of social communication and condomless anal intercourse (CAI) among MSM and the implications for prevention and control of HIV among MSM in China using an egocentric network framework.

Methods

The data were collected in Guangzhou between November 2016 and May 2017 through standardized face-to-face interviews. The participants were recruited among MSM who received voluntary counselling and testing services (VCT) provided by nongovernmental organizations (NGOs) and the local Centers for Disease Control and Prevention (CDC). We used the framework of the egocentric network analysis, the odd ratios of CAI were analyzed using generalized estimating equations (GEE).

Results

In total, 1073 MSM who nominated 2667 sexual partners were sampled. MSM who were approximately 30 years old and chose sexual partners of different age category were more likely to engage in CAI. Participants with high level education who were in partnerships with individuals with lower education levels had a higher risk of CAI. Participants who reported having a strong relationship with their sexual partners(AOR = 1.31) were associated with a higher probability of experiencing CAI during sex; while having sexual partners who were unmarried (OR = 0.56), and participants who reported meeting sexual partners online (AOR = 0.74) or, having sex with an occasional partner (AOR = 0.44)were less likely to engage in CAI.

Conclusion

Our study indicates that the strength of sexual dyadic relational ties and different social communication mixing patterns across ages, educational categories, and marital status were associated with CAI.

Electronic supplementary material

The online version of this article (10.1186/s12889-019-7082-9) contains supplementary material, which is available to authorized users.

Pockets of HIV Non-infection Within Highly-Infected Risk Networks in Athens, Greece

As part of a network study of HIV infection among people who inject drugs (PWID) and their contacts, we discovered a connected subcomponent of 29 uninfected PWID. In the context of a just-declining large epidemic outbreak, this raised a question: What explains the existence of large pockets of uninfected people? Possible explanations include “firewall effects” (Friedman et al., 2000; Dombrowski et al., 2017) wherein the only HIV+ people that the uninfected take risks with have low viral loads; “bottleneck effects” wherein few network paths into the pocket of non-infection exist; low levels of risk behavior; and an impending outbreak. We considered each of these. Participants provided information on their enhanced sexual and injection networks and assisted us in recruiting network members. The largest connected component had 241 members. Data on risk behaviors in the last 6 months were collected at the individual level. Recent infection was determined by LAg (Sedia^TM Biosciences Corporation), data on recent seronegative tests, and viral load. HIV RNA was quantified using Artus HI Virus-1 RG RT-PCR (Qiagen). The 29 members of the connected subcomponent of uninfected participants were connected (network distance = 1) to 17 recently-infected and 24 long-term infected participants. Fourteen (48%) of these 29 uninfected were classified as “extremely high risk” because they self-reported syringe sharing and had at least one injection partner with viral load >100,000 copies/mL who also reported syringe sharing. Seventeen of the 29 uninfected were re-interviewed after 6 months, but none had seroconverted. These findings show the power of network research in discovering infection patterns that standard individual-level studies cannot. Theoretical development and exploratory network research studies may be needed to understand these findings and deepen our understanding of how HIV does and does not spread through communities. Finally, the methods developed here provide practical tools to study “bottleneck” and “firewall” network hypotheses in practice.

Statistical adjustment of network degree in respondent-driven sampling estimators: venue attendance as a proxy for network size among young MSM

We introduce a new venue-informed network degree measure, which we applied to respondent-driven sampling (RDS) estimators. Using data collected from 746 young MSM in 2014-2016 in Chicago, IL, and Houston, TX, we estimated the population seroprevalence of HIV and syphilis and risk/protective behaviors, using RDS estimates with self-reported network size as a standard degree measure as well as our proposed venue-informed degree measure. The results indicate that the venue-informed degree measure tended to be more efficient (smaller variance) and less biased than the other measure in both cities sampled. Venue attendance-adjusted network size may provide a more reliable and accurate degree measure for RDS estimates of the outcomes of interest.

Unraveling the Complexity of Wildland Urban Interface Fires

Recent wildland urban interface fires have demonstrated the unrelenting destructive nature of these events and have called for an urgent need to address the problem. The Wildfire paradox reinforces the ideology that forest fires are inevitable and are actually beneficial; therefore focus should to be shifted towards minimizing potential losses to communities. This requires the development of vulnerability-based frameworks that can be used to provide holistic understanding of risk. In this study, we devise a probabilistic approach for quantifying community vulnerability to wildfires by applying concepts of graph theory. A directed graph for community in question is developed to model wildfire inside a community by incorporating different fire propagation modes. The model accounts for relevant community-specific characteristics including wind conditions, community layout, individual structural features, and the surrounding wildland vegetation. We calibrate the framework to study the infamous 1991 Oakland fire in an attempt to unravel the complexity of community fires. We use traditional centrality measures to identify critical behavior patterns and to evaluate the effect of fire mitigation strategies. Unlike current practice, the results are shown to be community-specific with substantial dependency of risk on meteorological conditions, environmental factors, and community characteristics and layout.

Measuring stigma affecting sex workers (SW) and men who have sex with men (MSM): A systematic review

Background

Stigma involves discrediting a person or group based on a perceived attribute, behaviour or reputation associated with them. Sex workers (SW) and men who have sex with men (MSM) are key populations who are often at increased risk for the acquisition and transmission of HIV and who are affected by stigma that can negatively impact their health and well-being. Although stigma was included as an indicator in the US National HIV/AIDS Strategic Plan and there have been consultations focused on adding a stigma indicator within PEPFAR and the Global Fund in relation to potentiating HIV risks among key populations, there remains limited consensus on the appropriate measurement of SW- or MSM-associated stigma. Consequently, this systematic review summarizes studies using quantitative, qualitative, or mixed methods approaches to measure stigma affecting sex workers and men who have sex with men.

Methods and findings

This systematic review included English, French, and Spanish peer-reviewed research of any study design measuring SW- or MSM-associated stigma. Articles were published from January 1, 2004 to March 26, 2014 in PsycINFO, PubMed, EMBASE, CINAHL Plus, Global Health, and World Health Organization Global Health Library Regional Indexes.

Of the 541 articles reviewed, the majority measured stigma toward MSM (over 97%), were conducted in North America, used quantitative methods, and focused on internalized stigma.

Conclusions

With the inclusion of addressing stigma in several domestic and international HIV strategies, there is a need to ensure the use of validated metrics for stigma. The field to date has completed limited measurement of stigma affecting sex workers, and limited measurement of stigma affecting MSM outside of higher income settings. Moving forward requires a concerted effort integrating validated metrics of stigma into health-related surveys and programs for key populations.

Discrete-time dynamic network model for the spread of susceptible-infective-recovered diseases

We propose a discrete-time dynamic network model describing the spread of susceptible-infective-recovered diseases in a population. We consider the case in which the nodes in the network change their links due to social mixing dynamics as well as in response to the disease. The model shows the behavior that, as we increase social mixing, disease spread is inhibited in certain cases, while in other cases it is enhanced. We also extend this dynamic network model to take into account the case of hidden infection. Here we find that, as expected, the disease spreads more readily if there is a time period after contracting the disease during which an individual is infective but is not known to have the disease.

A comparison of network sampling designs for a hidden population of drug users: Random walk vs. respondent-driven sampling

Both random walk and respondent-driven sampling (RDS) exploit social networks and may reduce biases introduced by earlier methods for sampling from hidden populations. Although RDS has become much more widely used by social researchers than random walk (RW), there has been little discussion of the tradeoffs in choosing RDS over RW. This paper compares experiences of implementing RW and RDS to recruit drug users to a network-based study in Houston, Texas. Both recruitment methods were implemented over comparable periods of time, with the same population, by the same research staff. RDS methods recruited more participants with less strain on staff. However, participants recruited through RW were more forthcoming than RDS participants in helping to recruit members of their social networks. Findings indicate that, dependent upon study goals, researchers' choice of design may influence participant recruitment, participant commitment, and impact on staff, factors that may in turn affect overall study success.

Social Networks Across Common Cancer Types: The Evidence, Gaps, and Areas of Potential Impact

Although the association between social context and health has been demonstrated previously, much less is known about network interactions by gender, race/ethnicity, and sociodemographic characteristics. Given the variability in cancer outcomes among groups, research on these relationships may have important implications for addressing cancer health disparities. We examined the literature on social networks and cancer across the cancer continuum among adults. Relevant studies (N=16) were identified using two common databases: PubMed and Google Scholar. Most studies used a prospective cohort study design (n=9), included women only (n=11), and were located in the United States (n=14). Seventy-five percent of the studies reviewed used a validated scale or validated items to measure social networks (n=12). Only one study examined social network differences by race, 57.1% (n=8) focused on breast cancer alone, 14.3% (n=2) explored colorectal cancer or multiple cancers simultaneously, and 7.1% (n=1) only prostate cancer. More than half of the studies included multiple ethnicities in the sample, while one study included only low-income subjects. Despite findings of associations between social networks and cancer survival, risk, and screening, none of the studies utilized social networks as a mechanism for reducing health disparities; however, such an approach has been utilized for infectious disease control. Social networks and the support provided within these networks have important implications for health behaviors and ultimately cancer disparities. This review serves as the first step toward dialog on social networks as a missing component in the social determinants of cancer disparities literature that could move the needle upstream to target adverse cancer outcomes among vulnerable populations.

Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks

Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection. As such, pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard (random-mixing) results. Recently, for SIR-type infections (that produce one epidemic in a closed population) on locally tree-like networks, these approximations have been shown to be exact. However, network models are ideally suited for Sexually Transmitted Infections (STIs) due to the greater level of detail available for sexual contact networks, and these diseases often possess SIS-type dynamics. Here, we consider the accuracy of three systematic approximations that can be applied to arbitrary disease dynamics, including SIS behaviour. We focus in particular on low degree networks, in which the small number of neighbours causes build-up of local correlations between the state of adjacent nodes that are challenging to capture. By examining how and when these approximation models converge to simulation results, we generate insights into the role of network structure in the infection dynamics of SIS-type infections.

Networks are now widely used to model infectious diseases, but have posed significant mathematical challenges. Recently analytic results have been obtained for ‘one-off’ network epidemics that follow the SIR paradigm, but these results do not carry over to other scenarios—most significantly to many sexually transmitted infections, where accounting for network structure is vital. Here, we show that it is possible to obtain the large-population dynamics of such diseases on networks through systematic approximations. We focus on a mathematically challenging case of SIS dynamics on networks with low degree.

Analyses of infectious disease patterns and drivers largely lack insights from social epidemiology: contemporary patterns and future opportunities

BACKGROUND

Infectious disease epidemiologists have long recognised the importance of social variables as drivers of epidemics and disease risk, yet few apply analytic approaches from social epidemiology. We quantified and evaluated the extent to which recent infectious disease research is employing the perspectives and methods of social epidemiology by replicating the methodology used by Cohen et al in a 2007 study.

METHODS

2 search strategies were used to identify and review articles published from 1 January 2005 to 31 December 2013. First, we performed a keyword search of 'social epidemiology' in the title/abstract/text of published studies identified in PubMed, PsychInfo and ISI Web of Science, and classified each study as pertaining to infectious, non-infectious or other outcomes. A second PubMed search identified articles that were cross-referenced under non-infectious or infectious, and search terms relating to social variables. The abstracts of all articles were read, classified and examined to identify patterns over time.

RESULTS

Findings suggest that infectious disease research publications that explicitly or implicitly incorporate social epidemiological approaches have stagnated in recent years. While the number of publications that were explicitly self-classified as 'social epidemiology' has risen, the proportion that investigated infectious disease outcomes has declined. Furthermore, infectious diseases accounted for the smallest proportion of articles that were cross-referenced with Medical Subject Headings (MeSH) terms related to social factors, and most of these involved sexually transmitted diseases.

CONCLUSIONS

The current landscape of infectious disease epidemiology could benefit from new approaches to understanding how the social and biophysical environment sustains transmission and exacerbates disparities. The framework of social epidemiology provides infectious disease researchers with such a perspective and research opportunity.

Reconstructing contact network parameters from viral phylogenies

Models of the spread of disease in a population often make the simplifying assumption that the population is homogeneously mixed, or is divided into homogeneously mixed compartments. However, human populations have complex structures formed by social contacts, which can have a significant influence on the rate of epidemic spread. Contact network models capture this structure by explicitly representing each contact which could possibly lead to a transmission. We developed a method based on approximate Bayesian computation (ABC), a likelihood-free inference strategy, for estimating structural parameters of the contact network underlying an observed viral phylogeny. The method combines adaptive sequential Monte Carlo for ABC, Gillespie simulation for propagating epidemics though networks, and a kernel-based tree similarity score. We used the method to fit the Barabási-Albert network model to simulated transmission trees, and also applied it to viral phylogenies estimated from ten published HIV sequence datasets. This model incorporates a feature called preferential attachment (PA), whereby individuals with more existing contacts accumulate new contacts at a higher rate. On simulated data, we found that the strength of PA and the number of infected nodes in the network can often be accurately estimated. On the other hand, the mean degree of the network, as well as the total number of nodes, was not estimable with ABC. We observed sub-linear PA power in all datasets, as well as higher PA power in networks of injection drug users. These results underscore the importance of considering contact structures when performing phylodynamic inference. Our method offers the potential to quantitatively investigate the contact network structure underlying viral epidemics.

Vulnerability, Social Networks, Sites, and Selling as Predictors of Drug use among Urban African American and Puerto Rican Emerging Adults

This paper reviews the results of research conducted with African American and Puerto Rican emerging adults between the ages of 18 and 25 whose life experiences increase vulnerability to drug use and pose some significant challenges in achieving milestones widely recognized as important in achieving adult status. Literature on drug use in adolescence suggests that personal vulnerability accounts for most experimental and problem drug use. Included in the vulnerability construct are religiosity, perceived risk, social influence, drug access, social norms, and social risk defined primarily as exposure to various forms of violence. This study shows that personal vulnerability explains only some of the variance in use and predicts variance differently with respect to different specific drugs and polydrug use. Further, it argues that additional contextual factors including social networks, party and club attendance, and drug selling activities, all typical of emerging adulthood and urban lifestyle, are also important factors in enhancing potential for accelerated drug use during this developmental period. Finally, it notes that the consequences of these activities have implications for further economic and social marginalization of urban, multiethnic low income emerging adults.

Mycobacterium Tuberculosis Infection, Immigration Status, and Diagnostic Discordance: A Comparison of Tuberculin Skin Test and QuantiFERON-TB Gold In-Tube Test Among Immigrants to the U.S

OBJECTIVE

We used a recent source of nationally representative population data on tuberculosis (TB) infection to characterize concordance between the tuberculin skin test (TST) and the QuantiFERON-TB Gold In-Tube (QFT-GIT) blood test for immigrants in the United States.

METHODS

We used TB screening data from the 2011-2012 National Health and Nutrition Examination Survey to examine concordance between the TST and QFT-GIT--an interferon-gamma release assay (IGRA) blood test--for 7,097 U.S. natives, naturalized citizens, and noncitizens.

RESULTS

Consistent with prior findings, one in five immigrants in the survey was identified with latent TB infection (LTBI), a rate 14 times higher than for U.S. natives. We also found higher rates of discordant TST/IGRA results among immigrants than among U.S. natives. Unadjusted discordance between TST and IGRA was 3% among U.S. natives (weighted N=5,684,274 of 191,179,213) but ranged up to 19% for noncitizens (weighted N=3,722,960 of 19,377,147). Adjusting for age, sex, and race/ethnicity, noncitizens had more than nine times the odds of having a positive TST result but negative QFT-GIT result compared with U.S. natives.

CONCLUSIONS

Our findings suggest that whether and how either of these tests should be deployed is highly context sensitive. Significant discordance in test results when used among immigrants raises the possibility of missed opportunities for harm reduction in this already at-risk population. However, we found little distinction between the tests in terms of diagnostic outcome when used in a U.S. native population, suggesting little benefit to the adoption and use of the QFT-GIT test in place of TST on the basis of test performance alone for this population.

Partnership capacity for community health improvement plan implementation: findings from a social network analysis

Background

Many health departments collaborate with community organizations on community health improvement processes. While a number of resources exist to plan and implement a community health improvement plan (CHIP), little empirical evidence exists on how to leverage and expand partnerships when implementing a CHIP. The purpose of this study was to identify characteristics of the network involved in implementing the CHIP in one large community. The aims of this analysis are to: 1) identify essential network partners (and thereby highlight potential network gaps), 2) gauge current levels of partner involvement, 3) understand and effectively leverage network resources, and 4) enable a data-driven approach for future collaborative network improvements.

Methods

We collected primary data via survey from n = 41 organizations involved in the Health Improvement Partnership of Maricopa County (HIPMC), in Arizona. Using the previously validated Program to Analyze, Record, and Track Networks to Enhance Relationships (PARTNER) tool, organizations provided information on existing ties with other coalition members, including frequency and depth of partnership and eight categories of perceived value/trust of each current partner organization.

Results

The coalition’s overall network had a density score of 30 %, degree centralization score of 73 %, and trust score of 81 %. Network maps are presented to identify existing relationships between HIPMC members according to partnership frequency and intensity, duration of involvement in the coalition, and self-reported contributions to the coalition. Overall, number of ties and other partnership measures were positively correlated with an organization’s perceived value and trustworthiness as rated by other coalition members.

Conclusions

Our study presents a novel use of social network analysis methods to evaluate the coalition of organizations involved in implementing a CHIP in an urban community. The large coalition had relatively low network density but high degree centralization—meaning key organizations link organizations otherwise not tightly partnering. Coalition members rated each other highly on trust, a positive sign for future partnership development efforts. Examination of network maps reveal key organizations that can be targeted for future partnership facilitation and expansion. Future network data collection will enable exploration of longitudinal trends and exploration of network characteristics versus health behavior, status, and outcome changes.

Situating Social Influence Processes: Dynamic, Multidirectional Flows of Influence Within Social Networks

Social psychologists have studied the psychological processes involved in persuasion, conformity, and other forms of social influence, but they have rarely modeled the ways influence processes play out when multiple sources and multiple targets of influence interact over time. However, workers in other fields from sociology and economics to cognitive science and physics have recognized the importance of social influence and have developed models of influence flow in populations and groups—generally without relying on detailed social psychological findings. This article reviews models of social influence from a number of fields, categorizing them using four conceptual dimensions to delineate the universe of possible models. The goal is to encourage interdisciplinary collaborations to build models that incorporate the detailed, microlevel understanding of influence processes derived from focused laboratory studies but contextualized in ways that recognize how multidirectional, dynamic influences are situated in people's social networks and relationships.